Influence of High-Risk Pathological Factors and their Interaction on the Survival Benefit of Adjuvant Chemotherapy in Stage II Rectal Cancer: A Retrospective Study.

Objectives: We investigated the impact of high-risk factors in stage II (TNM stage) rectal cancer patients to determine whether they benefit from adjuvant chemotherapy after surgery. Additionally, we explored the interaction between high-risk factors and adjuvant chemotherapy. Our study provides refined guidance for postoperative treatment in patients with stage II rectal cancer. Methods: The retrospective study included 570 stage II rectal adenocarcinoma patients who underwent total mesorectal excision surgery at Tianjin Union Medical Center from August 2012 to July 2019. We employed Cox regression models to assess the collected pathological and clinical factors, identifying the risk factors for overall survival (OS) and disease-free survival (DFS). Additionally, we thoroughly examined the interaction between various high-risk pathological factors and postoperative chemotherapy (ACT), including multiplicative interaction (INTM) and additive interaction (RERI). Results: Among the 570 stage II rectal cancer patients in this study, the average age was 62 years, with 58.9% (N=336) of the population being older than 60. Males accounted for the majority at 64.9% (N=370). Age was found to have an impact on whether patients received adjuvant chemotherapy after surgery (P<=0.001).Furthermore, age (HR: 1.916, 95% CI: 1.158-3.173, P=0.011; HR: 1.881, 95% CI: 1.111-3.186, P=0.019), TNM stage (HR: 2.216, 95% CI: 1.003-4.897, P=0.029; HR: 2.276, 95% CI: 1.026-5.048, P=0.043), the number of lymph nodes cleared during surgery (HR: 1.968, 95% CI: 1.112-3.483, P=0.017; HR: 1.864, 95% CI: 0.995-3.493, P=0.045), and lymphovascular invasion (HR: 2.864, 95% CI: 1.567-5.232, P=0.001; HR: 3.161, 95% CI: 1.723-5.799, P<0.001) were identified as independent risk factors for patients' overall survival (OS) and disease-free survival (DFS). Moreover, the interaction analysis, both multiplicative and additive, revealed significant interactions between the number of lymph nodes cleared during surgery and the administration of adjuvant chemotherapy. For OS (HR for multiplicative interaction: 0.477, p=0.045; RERI: -0.531, 95% CI: -1.061, -0.002) and for DFS (HR for multiplicative interaction: 0.338, p=0.039; RERI: -1.097, 95% CI: -2.190, -0.005). Conclusions: This study provides insights into the complex relationship between adjuvant chemotherapy (ACT) and survival outcomes in stage II rectal cancer patients with high-risk pathological factors. The findings suggest that the number of cleared lymph nodes plays a significant role in the efficacy of ACT and underscores the need for individualized treatment decisions in this patient population.

Recent advances and challenges in protein complex model accuracy estimation.

Estimation of model accuracy plays a crucial role in protein structure prediction, aiming to evaluate the quality of predicted protein structure models accurately and objectively. This process is not only key to screening candidate models that are close to the real structure, but also provides guidance for further optimization of protein structures. With the significant advancements made by AlphaFold2 in monomer structure, the problem of single-domain protein structure prediction has been widely solved. Correspondingly, the importance of assessing the quality of single-domain protein models decreased, and the research focus has shifted to estimation of model accuracy of protein complexes. In this review, our goal is to provide a comprehensive overview of the reference and statistical metrics, as well as representative methods, and the current challenges within four distinct facets (Topology Global Score, Interface Total Score, Interface Residue-Wise Score, and Tertiary Residue-Wise Score) in the field of complex EMA.

SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition.

Protein sequence design can provide valuable insights into biopharmaceuticals and disease treatments. Currently, most protein sequence design methods based on deep learning focus on network architecture optimization, while ignoring protein-specific physicochemical features. Inspired by the successful application of structure templates and pre-trained models in the protein structure prediction, we explored whether the representation of structural sequence profile can be used for protein sequence design. In this work, we propose SPDesign, a method for protein sequence design based on structural sequence profile using ultrafast shape recognition. Given an input backbone structure, SPDesign utilizes ultrafast shape recognition vectors to accelerate the search for similar protein structures in our in-house PAcluster80 structure database and then extracts the sequence profile through structure alignment. Combined with structural pre-trained knowledge and geometric features, they are further fed into an enhanced graph neural network for sequence prediction. The results show that SPDesign significantly outperforms the state-of-the-art methods, such as ProteinMPNN, Pifold and LM-Design, leading to 21.89%, 15.54% and 11.4% accuracy gains in sequence recovery rate on CATH 4.2 benchmark, respectively. Encouraging results also have been achieved on orphan and de novo (designed) benchmarks with few homologous sequences. Furthermore, analysis conducted by the PDBench tool suggests that SPDesign performs well in subdivided structures. More interestingly, we found that SPDesign can well reconstruct the sequences of some proteins that have similar structures but different sequences. Finally, the structural modeling verification experiment indicates that the sequences designed by SPDesign can fold into the native structures more accurately.

Protein Multiple Conformation Prediction Using Multi-Objective Evolution Algorithm.

The breakthrough of AlphaFold2 and the publication of AlphaFold DB represent a significant advance in the field of predicting static protein structures. However, AlphaFold2 models tend to represent a single static structure, and multiple-conformation prediction remains a challenge. In this work, we proposed a method named MultiSFold, which uses a distance-based multi-objective evolutionary algorithm to predict multiple conformations. To begin, multiple energy landscapes are constructed using different competing constraints generated by deep learning. Subsequently, an iterative modal exploration and exploitation strategy is designed to sample conformations, incorporating multi-objective optimization, geometric optimization and structural similarity clustering. Finally, the final population is generated using a loop-specific sampling strategy to adjust the spatial orientations. MultiSFold was evaluated against state-of-the-art methods using a benchmark set containing 80 protein targets, each characterized by two representative conformational states. Based on the proposed metric, MultiSFold achieves a remarkable success ratio of 56.25% in predicting multiple conformations, while AlphaFold2 only achieves 10.00%, which may indicate that conformational sampling combined with knowledge gained through deep learning has the potential to generate conformations spanning the range between different conformational states. In addition, MultiSFold was tested on 244 human proteins with low structural accuracy in AlphaFold DB to test whether it could further improve the accuracy of static structures. The experimental results demonstrate the performance of MultiSFold, with a TM-score better than that of AlphaFold2 by 2.97% and RoseTTAFold by 7.72%. The online server is at http://zhanglab-bioinf.com/MultiSFold .

Recent Advances and Challenges in Protein Structure Prediction.

Artificial intelligence has made significant advances in the field of protein structure prediction in recent years. In particular, DeepMind's end-to-end model, AlphaFold2, has demonstrated the capability to predict three-dimensional structures of numerous unknown proteins with accuracy levels comparable to those of experimental methods. This breakthrough has opened up new possibilities for understanding protein structure and function as well as accelerating drug discovery and other applications in the field of biology and medicine. Despite the remarkable achievements of artificial intelligence in the field, there are still some challenges and limitations. In this Review, we discuss the recent progress and some of the challenges in protein structure prediction. These challenges include predicting multidomain protein structures, protein complex structures, multiple conformational states of proteins, and protein folding pathways. Furthermore, we highlight directions in which further improvements can be conducted.

Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning.

Accurately capturing domain-domain interactions is key to understanding protein function and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on single domain, it should be noted that the structure modeling for multi-domain protein and complex remains a challenge. In this study, we developed a multi-domain and complex structure assembly protocol, named DeepAssembly, based on domain segmentation and single domain modeling algorithms. Firstly, DeepAssembly uses a population-based evolutionary algorithm to assemble multi-domain proteins by inter-domain interactions inferred from a developed deep learning network. Secondly, protein complexes are assembled by means of domains rather than chains using DeepAssembly. Experimental results show that on 219 multi-domain proteins, the average inter-domain distance precision by DeepAssembly is 22.7% higher than that of AlphaFold2. Moreover, DeepAssembly improves accuracy by 13.1% for 164 multi-domain structures with low confidence deposited in AlphaFold database. We apply DeepAssembly for the prediction of 247 heterodimers. We find that DeepAssembly successfully predicts the interface (DockQ ≥ 0.23) for 32.4% of the dimers, suggesting a lighter way to assemble complex structures by treating domains as assembly units and using inter-domain interactions learned from monomer structures.

Factors influencing advanced colorectal neoplasm anatomic site distribution in China: An epidemiological study based on colorectal cancer screening data.

OBJECTIVE: Existing studies indicate that advanced colorectal neoplasms exhibit distinct clinical and biological traits based on anatomical sites. However, in China, especially for advanced colorectal neoplasms, there's limited information available on these traits. Our primary objective is to comprehensively study the characteristics of advanced colorectal neoplasm patients in different anatomical sites in China. METHODS: We selected information from the colorectal cancer screening database in Tianjin, China, since 2010 as the study subject. We chose valid information from 3113 patients with comprehensive data and diagnosed advanced colorectal neoplasms (ANs) from a pool of 19,308 individuals to be included in the study. We then conducted further analysis to examine the correlation between these epidemiological data and tumor location. RESULTS: Among the 3113 patients, neoplasms in the left side of the colon accounted for the largest proportion, while neoplasms in the right side of the colon had the smallest proportion, followed by rectal neoplasms. The highest proportion of advanced colorectal neoplasms was found among men. In the age group of 39-49 years old, the proportion of left late-stage advanced colon neoplasms was equal to that of right late-stage advanced colon neoplasms, while late-stage advanced rectal neoplasms increased with age. Smoking, drinking, and a history of colon cancer in first-degree relatives showed statistically significant associations with the location distribution of advanced colorectal neoplasms. A history of appendicitis, appendectomy, cholecystitis, or cholecystectomy did not significantly affect the location distribution of advanced colorectal neoplasms. However, among patients with such histories, there was a statistically significant relationship between advanced colon neoplasms on the right and those on the left and in the rectum. Similar results were observed for BMI. CONCLUSION: Our research findings demonstrate that advanced colorectal neoplasms display unique epidemiological characteristics depending on their anatomical locations, and these distinctions deviate from those observed in Western populations. These insights contribute to a more comprehensive understanding of the topic and offer valuable guidance for future research in China. We advocate for further investigations centered on the anatomical location of colorectal neoplasms to enhance the precision of colorectal cancer (CRC) screening and treatment.

Recent Advances in Protein Folding Pathway Prediction through Computational Methods.

The protein folding mechanisms are crucial to understanding the fundamental processes of life and solving many biological and medical problems. By studying the folding process, we can reveal how proteins achieve their biological functions through specific structures, providing insights into the treatment and prevention of diseases. With the advancement of AI technology in the field of protein structure prediction, computational methods have become increasingly important and promising for studying protein folding mechanisms. In this review, we retrospect the current progress in the field of protein folding mechanisms by computational methods from four perspectives: simulation of an inverse folding pathway from native state to unfolded state; prediction of early folding residues by machine learning; exploration of protein folding pathways through conformational sampling; prediction of protein folding intermediates based on templates. Finally, the challenges and future perspectives of the protein folding problem by computational methods are also discussed.

Pathfinder: Protein folding pathway prediction based on conformational sampling.

The study of protein folding mechanism is a challenge in molecular biology, which is of great significance for revealing the movement rules of biological macromolecules, understanding the pathogenic mechanism of folding diseases, and designing protein engineering materials. Based on the hypothesis that the conformational sampling trajectory contain the information of folding pathway, we propose a protein folding pathway prediction algorithm named Pathfinder. Firstly, Pathfinder performs large-scale sampling of the conformational space and clusters the decoys obtained in the sampling. The heterogeneous conformations obtained by clustering are named seed states. Then, a resampling algorithm that is not constrained by the local energy basin is designed to obtain the transition probabilities of seed states. Finally, protein folding pathways are inferred from the maximum transition probabilities of seed states. The proposed Pathfinder is tested on our developed test set (34 proteins). For 11 widely studied proteins, we correctly predicted their folding pathways and specifically analyzed 5 of them. For 13 proteins, we predicted their folding pathways to be further verified by biological experiments. For 6 proteins, we analyzed the reasons for the low prediction accuracy. For the other 4 proteins without biological experiment results, potential folding pathways were predicted to provide new insights into protein folding mechanism. The results reveal that structural analogs may have different folding pathways to express different biological functions, homologous proteins may contain common folding pathways, and α-helices may be more prone to early protein folding than ß-strands.

The accuracy of the FIT in detecting advanced neoplasm is highest in young people aged 40 to 49 years: an analysis based on sex and age.

BACKGROUND: Colorectal cancer (CRC) is one of the most common cancers and is associated with high incidence and mortality rates worldwide. CRC has caused a tremendous loss of human health and wealth. The incidence and mortality of colorectal carcinoma are increasing in young adults. Early cancer detection and prevention are made possible through screening. At present, the faecal immunochemical test (FIT) is a noninvasive method that can be used for the large-scale clinical screening of CRC status. Therefore, this study, based on CRC screening results in Tianjin from 2012 to 2020, was conducted to analyse the major differences in diagnostic performance parameters according to sex and age. METHODS: This study was based on 39,991 colonoscopies performed for individuals in the Tianjin CRC screening program from 2012 to 2020. Of these individuals, they had complete FIT and colonoscopy results. The differences in FIT results were analysed by sex and age. RESULTS: According to this study, males were generally more likely to develop advanced neoplasms (ANs) than females, and the prevalence increased with age. Males with negative FIT results were more likely to have advanced neoplasms than females with positive results. The accuracy of the FIT in detecting ANs in each age group was 54.9%, 45.5%, 48.6% and 49.5% in the 40-49, 50-59, 60-69, and ≥ 70 age groups, respectively. CONCLUSIONS: The FIT detected ANs with highest accuracy in the 40-49 age group. Our research can provide guidance to formulate CRC screening strategies.

Protein structure and folding pathway prediction based on remote homologs recognition using PAthreader.

Recognition of remote homologous structures is a necessary module in AlphaFold2 and is also essential for the exploration of protein folding pathways. Here, we propose a method, PAthreader, to recognize remote templates and explore folding pathways. Firstly, we design a three-track alignment between predicted distance profiles and structure profiles extracted from PDB and AlphaFold DB, to improve the recognition accuracy of remote templates. Secondly, we improve the performance of AlphaFold2 using the templates identified by PAthreader. Thirdly, we explore protein folding pathways based on our conjecture that dynamic folding information of protein is implicitly contained in its remote homologs. The results show that the average accuracy of PAthreader templates is 11.6% higher than that of HHsearch. In terms of structure modelling, PAthreader outperform AlphaFold2 and ranks first on the CAMEO blind test for the latest three months. Furthermore, we predict protein folding pathways for 37 proteins, in which the results of 7 proteins are almost consistent with those of biological experiments, and the other 30 human proteins have yet to be verified by biological experiments, revealing that folding information can be exploited from remote homologous structures.

Improved model quality assessment using sequence and structural information by enhanced deep neural networks.

Protein model quality assessment plays an important role in protein structure prediction, protein design and drug discovery. In this work, DeepUMQA2, a substantially improved version of DeepUMQA for protein model quality assessment, is proposed. First, sequence features containing protein co-evolution information and structural features reflecting family information are extracted to complement model-dependent features. Second, a novel backbone network based on triangular multiplication update and axial attention mechanism is designed to enhance information exchange between inter-residue pairs. On CASP13 and CASP14 datasets, the performance of DeepUMQA2 increases by 20.5 and 20.4% compared with DeepUMQA, respectively (measured by top 1 loss). Moreover, on the three-month CAMEO dataset (11 March to 04 June 2022), DeepUMQA2 outperforms DeepUMQA by 15.5% (measured by local AUC0,0.2) and ranks first among all competing server methods in CAMEO blind test. Experimental results show that DeepUMQA2 outperforms state-of-the-art model quality assessment methods, such as ProQ3D-LDDT, ModFOLD8, and DeepAccNet and DeepUMQA2 can select more suitable best models than state-of-the-art protein structure methods, such as AlphaFold2, RoseTTAFold and I-TASSER, provided themselves.

Construct a variable-length fragment library for de novo protein structure prediction.

Although remarkable achievements, such as AlphaFold2, have been made in end-to-end structure prediction, fragment libraries remain essential for de novo protein structure prediction, which can help explore and understand the protein-folding mechanism. In this work, we developed a variable-length fragment library (VFlib). In VFlib, a master structure database was first constructed from the Protein Data Bank through sequence clustering. The hidden Markov model (HMM) profile of each protein in the master structure database was generated by HHsuite, and the secondary structure of each protein was calculated by DSSP. For the query sequence, the HMM-profile was first constructed. Then, variable-length fragments were retrieved from the master structure database through dynamically variable-length profile-profile comparison. A complete method for chopping the query HMM-profile during this process was proposed to obtain fragments with increased diversity. Finally, secondary structure information was used to further screen the retrieved fragments to generate the final fragment library of specific query sequence. The experimental results obtained with a set of 120 nonredundant proteins show that the global precision and coverage of the fragment library generated by VFlib were 55.04% and 94.95% at the RMSD cutoff of 1.5 Å, respectively. Compared with the benchmark method of NNMake, the global precision of our fragment library had increased by 62.89% with equivalent coverage. Furthermore, the fragments generated by VFlib and NNMake were used to predict structure models through fragment assembly. Controlled experimental results demonstrate that the average TM-score of VFlib was 16.00% higher than that of NNMake.

A de novo protein structure prediction by iterative partition sampling, topology adjustment and residue-level distance deviation optimization.

MOTIVATION: With the great progress of deep learning-based inter-residue contact/distance prediction, the discrete space formed by fragment assembly cannot satisfy the distance constraint well. Thus, the optimal solution of the continuous space may not be achieved. Designing an effective closed-loop continuous dihedral angle optimization strategy that complements the discrete fragment assembly is crucial to improve the performance of the distance-assisted fragment assembly method. RESULTS: In this article, we proposed a de novo protein structure prediction method called IPTDFold based on closed-loop iterative partition sampling, topology adjustment and residue-level distance deviation optimization. First, local dihedral angle crossover and mutation operators are designed to explore the conformational space extensively and achieve information exchange between the conformations in the population. Then, the dihedral angle rotation model of loop region with partial inter-residue distance constraints is constructed, and the rotation angle satisfying the constraints is obtained by differential evolution algorithm, so as to adjust the spatial position relationship between the secondary structures. Finally, the residue distance deviation is evaluated according to the difference between the conformation and the predicted distance, and the dihedral angle of the residue is optimized with biased probability. The final model is generated by iterating the above three steps. IPTDFold is tested on 462 benchmark proteins, 24 FM targets of CASP13 and 20 FM targets of CASP14. Results show that IPTDFold is significantly superior to the distance-assisted fragment assembly method Rosetta_D (Rosetta with distance). In particular, the prediction accuracy of IPTDFold does not decrease as the length of the protein increases. When using the same FastRelax protocol, the prediction accuracy of IPTDFold is significantly superior to that of trRosetta without orientation constraints, and is equivalent to that of the full version of trRosetta. AVAILABILITYAND IMPLEMENTATION: The source code and executable are freely available at https://github.com/iobio-zjut/IPTDFold. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MMpred: a distance-assisted multimodal conformation sampling for de novo protein structure prediction.

MOTIVATION: The mathematically optimal solution in computational protein folding simulations does not always correspond to the native structure, due to the imperfection of the energy force fields. There is therefore a need to search for more diverse suboptimal solutions in order to identify the states close to the native. We propose a novel multimodal optimization protocol to improve the conformation sampling efficiency and modeling accuracy of de novo protein structure folding simulations. RESULTS: A distance-assisted multimodal optimization sampling algorithm, MMpred, is proposed for de novo protein structure prediction. The protocol consists of three stages: The first is a modal exploration stage, in which a structural similarity evaluation model DMscore is designed to control the diversity of conformations, generating a population of diverse structures in different low-energy basins. The second is a modal maintaining stage, where an adaptive clustering algorithm MNDcluster is proposed to divide the populations and merge the modal by adjusting the annealing temperature to locate the promising basins. In the last stage of modal exploitation, a greedy search strategy is used to accelerate the convergence of the modal. Distance constraint information is used to construct the conformation scoring model to guide sampling. MMpred is tested on a large set of 320 non-redundant proteins, where MMpred obtains models with TM-score≥0.5 on 291 cases, which is 28% higher than that of Rosetta guided with the same set of distance constraints. In addition, on 320 benchmark proteins, the enhanced version of MMpred (E-MMpred) has 167 targets better than trRosetta when the best of five models are evaluated. The average TM-score of the best model of E-MMpred is 0.732, which is comparable to trRosetta (0.730). AVAILABILITY AND IMPLEMENTATION: The source code and executable are freely available at https://github.com/iobio-zjut/MMpred. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Empirical Mode Decomposition Based Multi-Modal Activity Recognition.

This paper aims to develop an activity recognition algorithm to allow parents to monitor their children at home after school. A common method used to analyze electroencephalograms is to use infinite impulse response filters to decompose the electroencephalograms into various brain wave components. However, nonlinear phase distortions will be introduced by these filters. To address this issue, this paper applies empirical mode decomposition to decompose the electroencephalograms into various intrinsic mode functions and categorize them into four groups. In addition, common features used to analyze electroencephalograms are energy and entropy. However, because there are only two features, the available information is limited. To address this issue, this paper extracts 11 different physical quantities from each group of intrinsic mode functions, and these are employed as the features. Finally, this paper uses the random forest to perform activity recognition. It is worth noting that the conventional approach for performing activity recognition is based on a single type of signal, which limits the recognition performance. In this paper, a multi-modal system based on electroencephalograms, image sequences, and motion signals is used for activity recognition. The numerical simulation results show that the percentage accuracies based on three types of signal are higher than those based on two types of signal or the individual signals. This demonstrates the advantages of using the multi-modal approach for activity recognition. In addition, our proposed empirical mode decomposition-based method outperforms the conventional filtering-based method. This demonstrates the advantages of using the nonlinear and adaptive time frequency approach for activity recognition.

A Fast Calibration Method for Phased Arrays by Using the Graph Coloring Theory.

Phased array radars are able to provide highly accurate airplane surveillance and tracking performance if they are properly calibrated. However, the ambient temperature variation and device aging could greatly deteriorate their performance. Currently, performing a calibration over a large-scale phased array with thousands of antennas is time-consuming. To facilitate the process, we propose a fast calibration method for phased arrays with omnidirectional radiation patterns based on the graph coloring theory. This method transforms the calibration problem into a coloring problem that aims at minimizing the number of used colors. By reusing the calibration time slots spatially, more than one omnidirectional antenna can perform calibration simultaneously. The simulation proves this method can prominently reduce total calibration time and recover the radiation pattern from amplitude and phase errors and noise. It is worth noting that the total calibration time consumed by the proposed method remains constant and is negligible compared with other calibration methods.