Accurate and efficient protein sequence design through learning concise local environment of residues.

MOTIVATION: Computational protein sequence design has been widely applied in rational protein engineering and increasing the design accuracy and efficiency is highly desired. RESULTS: Here, we present ProDESIGN-LE, an accurate and efficient approach to protein sequence design. ProDESIGN-LE adopts a concise but informative representation of the residue's local environment and trains a transformer to learn the correlation between local environment of residues and their amino acid types. For a target backbone structure, ProDESIGN-LE uses the transformer to assign an appropriate residue type for each position based on its local environment within this structure, eventually acquiring a designed sequence with all residues fitting well with their local environments. We applied ProDESIGN-LE to design sequences for 68 naturally occurring and 129 hallucinated proteins within 20 s per protein on average. The designed proteins have their predicted structures perfectly resembling the target structures with a state-of-the-art average TM-score exceeding 0.80. We further experimentally validated ProDESIGN-LE by designing five sequences for an enzyme, chloramphenicol O-acetyltransferase type III (CAT III), and recombinantly expressing the proteins in Escherichia coli. Of these proteins, three exhibited excellent solubility, and one yielded monomeric species with circular dichroism spectra consistent with the natural CAT III protein. AVAILABILITY AND IMPLEMENTATION: The source code of ProDESIGN-LE is available at https://github.com/bigict/ProDESIGN-LE.

Seq-SetNet: directly exploiting multiple sequence alignment for protein secondary structure prediction.

MOTIVATION: Accurate prediction of protein structure relies heavily on exploiting multiple sequence alignment (MSA) for residue mutations and correlations as this information specifies protein tertiary structure. The widely used prediction approaches usually transform MSA into inter-mediate models, say position-specific scoring matrix or profile hidden Markov model. These inter-mediate models, however, cannot fully represent residue mutations and correlations carried by MSA; hence, an effective way to directly exploit MSAs is highly desirable. RESULTS: Here, we report a novel sequence set network (called Seq-SetNet) to directly and effectively exploit MSA for protein structure prediction. Seq-SetNet uses an 'encoding and aggregation' strategy that consists of two key elements: (i) an encoding module that takes a component homologue in MSA as input, and encodes residue mutations and correlations into context-specific features for each residue; and (ii) an aggregation module to aggregate the features extracted from all component homologues, which are further transformed into structural properties for residues of the query protein. As Seq-SetNet encodes each homologue protein individually, it could consider both insertions and deletions, as well as long-distance correlations among residues, thus representing more information than the inter-mediate models. Moreover, the encoding module automatically learns effective features and thus avoids manual feature engineering. Using symmetric aggregation functions, Seq-SetNet processes the homologue proteins as a sequence set, making its prediction results invariable to the order of these proteins. On popular benchmark sets, we demonstrated the successful application of Seq-SetNet to predict secondary structure and torsion angles of residues with improved accuracy and efficiency. AVAILABILITY AND IMPLEMENTATION: The code and datasets are available through https://github.com/fusong-ju/Seq-SetNet. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Organoids as a Robust Preclinical Model for Precision Medicine in Colorectal Cancer: A Systematic Review.

BACKGROUND: Patients with locally advanced or metastatic colorectal cancer (CRC) display heterogeneous responses to standard-of-care therapy. Robust preclinical models of malignancy in the form of patient-derived tumor organoids (PDTOs) have recently come to the fore in tailoring patient care to a personalized medicine level. This study aimed to review the literature systematically regarding PTDOs and gauge their impact on precision medicine in the management of CRC. METHODS: A PRISMA-compliant systematic review of the MEDLINE, EMBASE, Web of Science, and Cochrane Library databases was performed. The results were categorized based on the primary objective of the individual studies as follows: organoid use in predicting effective hyperthermic intraperitoneal chemotherapy (HIPEC), systemic chemotherapy in CRC, or neoadjuvant chemoradiotherapy in rectal cancer. RESULTS: The literature search found 200 publications, 16 of which met the inclusion criteria. Organoid models of primary and metastatic CRC have been increasingly used to assess clinical responses to standard therapy. Marked heterogeneity exists, matching the responses observed in clinical practice with ex vivo drug and radiation screening. Repeated correlation between organoid and patient sensitivity to forms of HIPEC, systemic chemotherapy, and chemoradiotherapy has been observed. CONCLUSION: Patient-derived tumor organoids are the latest tool in predictive translational research. Current organoid-based studies in precision medicine have shown their great potential for predicting the clinical response of patients to CRC therapy. Larger-scale, prospective data are required to fully support this exciting avenue in cancer care.

ISSEC: inferring contacts among protein secondary structure elements using deep object detection.

BACKGROUND: The formation of contacts among protein secondary structure elements (SSEs) is an important step in protein folding as it determines topology of protein tertiary structure; hence, inferring inter-SSE contacts is crucial to protein structure prediction. One of the existing strategies infers inter-SSE contacts directly from the predicted possibilities of inter-residue contacts without any preprocessing, and thus suffers from the excessive noises existing in the predicted inter-residue contacts. Another strategy defines SSEs based on protein secondary structure prediction first, and then judges whether each candidate SSE pair could form contact or not. However, it is difficult to accurately determine boundary of SSEs due to the errors in secondary structure prediction. The incorrectly-deduced SSEs definitely hinder subsequent prediction of the contacts among them. RESULTS: We here report an accurate approach to infer the inter-SSE contacts (thus called as ISSEC) using the deep object detection technique. The design of ISSEC is based on the observation that, in the inter-residue contact map, the contacting SSEs usually form rectangle regions with characteristic patterns. Therefore, ISSEC infers inter-SSE contacts through detecting such rectangle regions. Unlike the existing approach directly using the predicted probabilities of inter-residue contact, ISSEC applies the deep convolution technique to extract high-level features from the inter-residue contacts. More importantly, ISSEC does not rely on the pre-defined SSEs. Instead, ISSEC enumerates multiple candidate rectangle regions in the predicted inter-residue contact map, and for each region, ISSEC calculates a confidence score to measure whether it has characteristic patterns or not. ISSEC employs greedy strategy to select non-overlapping regions with high confidence score, and finally infers inter-SSE contacts according to these regions. CONCLUSIONS: Comprehensive experimental results suggested that ISSEC outperformed the state-of-the-art approaches in predicting inter-SSE contacts. We further demonstrated the successful applications of ISSEC to improve prediction of both inter-residue contacts and tertiary structure as well.

Future directions in advanced penile cancer - mechanisms of carcinogenesis and a search for targeted therapy.

Penile squamous cell carcinoma (SCC) is a rare and aggressive urological malignancy. Advanced penile SCC requires multimodal management, including surgery and systemic therapy. Given its rarity, there have been few substantial advances in our understanding of the molecular and genomic drivers of penile SCC, especially for patients with relapsed or advanced disease. In this review, we discuss the molecular and genomic landscape of penile SCC, clinical trials in progress and implications for novel therapeutic targets. Future work should focus on preclinical models to provide a platform for investigation and validation of new molecular pathways for testing of therapeutics.

Constructing effective energy functions for protein structure prediction through broadening attraction-basin and reverse Monte Carlo sampling.

BACKGROUND: The ab initio approaches to protein structure prediction usually employ the Monte Carlo technique to search the structural conformation that has the lowest energy. However, the widely-used energy functions are usually ineffective for conformation search. How to construct an effective energy function remains a challenging task. RESULTS: Here, we present a framework to construct effective energy functions for protein structure prediction. Unlike existing energy functions only requiring the native structure to be the lowest one, we attempt to maximize the attraction-basin where the native structure lies in the energy landscape. The underlying rationale is that each energy function determines a specific energy landscape together with a native attraction-basin, and the larger the attraction-basin is, the more likely for the Monte Carlo search procedure to find the native structure. Following this rationale, we constructed effective energy functions as follows: i) To explore the native attraction-basin determined by a certain energy function, we performed reverse Monte Carlo sampling starting from the native structure, identifying the structural conformations on the edge of attraction-basin. ii) To broaden the native attraction-basin, we smoothened the edge points of attraction-basin through tuning weights of energy terms, thus acquiring an improved energy function. Our framework alternates the broadening attraction-basin and reverse sampling steps (thus called BARS) until the native attraction-basin is sufficiently large. We present extensive experimental results to show that using the BARS framework, the constructed energy functions could greatly facilitate protein structure prediction in improving the quality of predicted structures and speeding up conformation search. CONCLUSION: Using the BARS framework, we constructed effective energy functions for protein structure prediction, which could improve the quality of predicted structures and speed up conformation search as well.

Correction to: Predicting protein inter-residue contacts using composite likelihood maximization and deep learning.

Following publication of the original article [1], the author explained that there are several errors in the original article.

Predicting protein inter-residue contacts using composite likelihood maximization and deep learning.

BACKGROUND: Accurate prediction of inter-residue contacts of a protein is important to calculating its tertiary structure. Analysis of co-evolutionary events among residues has been proved effective in inferring inter-residue contacts. The Markov random field (MRF) technique, although being widely used for contact prediction, suffers from the following dilemma: the actual likelihood function of MRF is accurate but time-consuming to calculate; in contrast, approximations to the actual likelihood, say pseudo-likelihood, are efficient to calculate but inaccurate. Thus, how to achieve both accuracy and efficiency simultaneously remains a challenge. RESULTS: In this study, we present such an approach (called clmDCA) for contact prediction. Unlike plmDCA using pseudo-likelihood, i.e., the product of conditional probability of individual residues, our approach uses composite-likelihood, i.e., the product of conditional probability of all residue pairs. Composite likelihood has been theoretically proved as a better approximation to the actual likelihood function than pseudo-likelihood. Meanwhile, composite likelihood is still efficient to maximize, thus ensuring the efficiency of clmDCA. We present comprehensive experiments on popular benchmark datasets, including PSICOV dataset and CASP-11 dataset, to show that: i) clmDCA alone outperforms the existing MRF-based approaches in prediction accuracy. ii) When equipped with deep learning technique for refinement, the prediction accuracy of clmDCA was further significantly improved, suggesting the suitability of clmDCA for subsequent refinement procedure. We further present a successful application of the predicted contacts to accurately build tertiary structures for proteins in the PSICOV dataset. CONCLUSIONS: Composite likelihood maximization algorithm can efficiently estimate the parameters of Markov Random Fields and can improve the prediction accuracy of protein inter-residue contacts.

Improving protein fold recognition by extracting fold-specific features from predicted residue-residue contacts.

MOTIVATION: Accurate recognition of protein fold types is a key step for template-based prediction of protein structures. The existing approaches to fold recognition mainly exploit the features derived from alignments of query protein against templates. These approaches have been shown to be successful for fold recognition at family level, but usually failed at superfamily/fold levels. To overcome this limitation, one of the key points is to explore more structurally informative features of proteins. Although residue-residue contacts carry abundant structural information, how to thoroughly exploit these information for fold recognition still remains a challenge. RESULTS: In this study, we present an approach (called DeepFR) to improve fold recognition at superfamily/fold levels. The basic idea of our approach is to extract fold-specific features from predicted residue-residue contacts of proteins using deep convolutional neural network (DCNN) technique. Based on these fold-specific features, we calculated similarity between query protein and templates, and then assigned query protein with fold type of the most similar template. DCNN has showed excellent performance in image feature extraction and image recognition; the rational underlying the application of DCNN for fold recognition is that contact likelihood maps are essentially analogy to images, as they both display compositional hierarchy. Experimental results on the LINDAHL dataset suggest that even using the extracted fold-specific features alone, our approach achieved success rate comparable to the state-of-the-art approaches. When further combining these features with traditional alignment-related features, the success rate of our approach increased to 92.3%, 82.5% and 78.8% at family, superfamily and fold levels, respectively, which is about 18% higher than the state-of-the-art approach at fold level, 6% higher at superfamily level and 1% higher at family level. An independent assessment on SCOP_TEST dataset showed consistent performance improvement, indicating robustness of our approach. Furthermore, bi-clustering results of the extracted features are compatible with fold hierarchy of proteins, implying that these features are fold-specific. Together, these results suggest that the features extracted from predicted contacts are orthogonal to alignment-related features, and the combination of them could greatly facilitate fold recognition at superfamily/fold levels and template-based prediction of protein structures. AVAILABILITY AND IMPLEMENTATION: Source code of DeepFR is freely available through https://github.com/zhujianwei31415/deepfr, and a web server is available through http://protein.ict.ac.cn/deepfr. CONTACT: zheng@itp.ac.cn or dbu@ict.ac.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Improving prediction of burial state of residues by exploiting correlation among residues.

BACKGROUND: Residues in a protein might be buried inside or exposed to the solvent surrounding the protein. The buried residues usually form hydrophobic cores to maintain the structural integrity of proteins while the exposed residues are tightly related to protein functions. Thus, the accurate prediction of solvent accessibility of residues will greatly facilitate our understanding of both structure and functionalities of proteins. Most of the state-of-the-art prediction approaches consider the burial state of each residue independently, thus neglecting the correlations among residues. RESULTS: In this study, we present a high-order conditional random field model that considers burial states of all residues in a protein simultaneously. Our approach exploits not only the correlation among adjacent residues but also the correlation among long-range residues. Experimental results showed that by exploiting the correlation among residues, our approach outperformed the state-of-the-art approaches in prediction accuracy. In-depth case studies also showed that by using the high-order statistical model, the errors committed by the bidirectional recurrent neural network and chain conditional random field models were successfully corrected. CONCLUSIONS: Our methods enable the accurate prediction of residue burial states, which should greatly facilitate protein structure prediction and evaluation.

FALCON@home: a high-throughput protein structure prediction server based on remote homologue recognition.

SUMMARY: The protein structure prediction approaches can be categorized into template-based modeling (including homology modeling and threading) and free modeling. However, the existing threading tools perform poorly on remote homologous proteins. Thus, improving fold recognition for remote homologous proteins remains a challenge. Besides, the proteome-wide structure prediction poses another challenge of increasing prediction throughput. In this study, we presented FALCON@home as a protein structure prediction server focusing on remote homologue identification. The design of FALCON@home is based on the observation that a structural template, especially for remote homologous proteins, consists of conserved regions interweaved with highly variable regions. The highly variable regions lead to vague alignments in threading approaches. Thus, FALCON@home first extracts conserved regions from each template and then aligns a query protein with conserved regions only rather than the full-length template directly. This helps avoid the vague alignments rooted in highly variable regions, improving remote homologue identification. We implemented FALCON@home using the Berkeley Open Infrastructure of Network Computing (BOINC) volunteer computing protocol. With computation power donated from over 20,000 volunteer CPUs, FALCON@home shows a throughput as high as processing of over 1000 proteins per day. In the Critical Assessment of protein Structure Prediction (CASP11), the FALCON@home-based prediction was ranked the 12th in the template-based modeling category. As an application, the structures of 880 mouse mitochondria proteins were predicted, which revealed the significant correlation between protein half-lives and protein structural factors. AVAILABILITY AND IMPLEMENTATION: FALCON@home is freely available at http://protein.ict.ac.cn/FALCON/. CONTACT: shuaicli@cityu.edu.hk, dbu@ict.ac.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Improving residue-residue contact prediction via low-rank and sparse decomposition of residue correlation matrix.

Strategies for correlation analysis in protein contact prediction often encounter two challenges, namely, the indirect coupling among residues, and the background correlations mainly caused by phylogenetic biases. While various studies have been conducted on how to disentangle indirect coupling, the removal of background correlations still remains unresolved. Here, we present an approach for removing background correlations via low-rank and sparse decomposition (LRS) of a residue correlation matrix. The correlation matrix can be constructed using either local inference strategies (e.g., mutual information, or MI) or global inference strategies (e.g., direct coupling analysis, or DCA). In our approach, a correlation matrix was decomposed into two components, i.e., a low-rank component representing background correlations, and a sparse component representing true correlations. Finally the residue contacts were inferred from the sparse component of correlation matrix. We trained our LRS-based method on the PSICOV dataset, and tested it on both GREMLIN and CASP11 datasets. Our experimental results suggested that LRS significantly improves the contact prediction precision. For example, when equipped with the LRS technique, the prediction precision of MI and mfDCA increased from 0.25 to 0.67 and from 0.58 to 0.70, respectively (Top L/10 predicted contacts, sequence separation: 5 AA, dataset: GREMLIN). In addition, our LRS technique also consistently outperforms the popular denoising technique APC (average product correction), on both local (MI_LRS: 0.67 vs MI_APC: 0.34) and global measures (mfDCA_LRS: 0.70 vs mfDCA_APC: 0.67). Interestingly, we found out that when equipped with our LRS technique, local inference strategies performed in a comparable manner to that of global inference strategies, implying that the application of LRS technique narrowed down the performance gap between local and global inference strategies. Overall, our LRS technique greatly facilitates protein contact prediction by removing background correlations. An implementation of the approach called COLORS (improving COntact prediction using LOw-Rank and Sparse matrix decomposition) is available from http://protein.ict.ac.cn/COLORS/.

The caffeic acid in aqueous extract of Tournefortia sarmentosa enhances neutrophil phagocytosis of Escherichia coli.

Tournefortia sarmentosa, a Chinese herbal medicine, is considered an antioxidant or a detoxicating agent. Recently T. sarmentosa has received attention for its effects on the immune response. Here we provide evidence that aqueous extract of T. sarmentosa can induce increased phagocytic uptake of Escherichia coli by differentiated HL-60 cells and by neutrophils. Our results also revealed that T. sarmentosa can inhibit E. coli survival within differentiated HL-60 cells. Furthermore, aqueous extract of T. sarmentosa has been shown to enhance cell surface Mac-1 expression and the activated AKT signaling pathway in E. coli-stimulated neutrophils. We also examined the effect of each constituents in aqueous extract of T. sarmentosa on phagocytic uptake of E. coli by differentiated HL-60 cells or neutrophils. Bacterial survival, cell surface Mac-1 expression, and AKT activation of neutrophils were also examined. Our results showed that caffeic acid is an important constituent in mediating aqueous extract of T. sarmentosa-induced phagocytic uptake. Taken together, these results suggest that aqueous extract of T. sarmentosa exerts effects that enhance inflammatory responses by improving phagocytic capability, inhibiting bacterial survival within cells, and increasing Mac-1 expression of neutrophils.

A Case Report on a Hybrid Approach to Managing Acute Large Bowel Obstruction Secondary to Spigelian Hernia.

Spigelian hernias are an uncommon type of primary ventral hernia and are defined as a defect in the Spigelian aponeurosis (fascia). Herein, we present an uncommon case of Spigelian hernia to highlight the potential complications of these hernias and the need for surgical management. This is a case report of an 86-year-old gentleman presenting post-fall with an acute rib fracture and an incidental Spigelian hernia seen on a CT trauma pan scan. The Spigelian hernia surgical treatment was planned for elective management due to the anesthetic risks associated with an elderly patient and acute rib fractures. Ultimately, the patient developed a large bowel obstruction secondary to the Spigelian hernia and required emergency operative management to relieve the obstruction. The patient had an uncomplicated recovery following his emergency surgery. This case report highlights the importance of assessing anesthetic risks versus surgical risks when it comes to surgical planning. Clinicians should recognize occult hernias and continue ongoing clinical reviews with a high index of suspicion, as symptoms of Spigelian hernia obstruction might be non-specific.

The utility of ctDNA in colorectal cancer with peritoneal metastases.

BACKGROUND: The development of peritoneal metastases (PM) in patients with colorectal cancer (CRC) connotates a poor prognosis. Circulating tumour (ctDNA) is a promising tumour biomarker in the management CRC. This systematic review aimed to summarize the role of ctDNA in patients with CRC and PM. METHODS: Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines, a systematic review of the literature until June 2022 was performed. Studies reporting on the utility of ctDNA in colorectal PM were included. A total of eight eligible studies were identified including a total of 167 patients. RESULTS: The findings from this review suggest an evolving role for ctDNA in CRC with PM. ctDNA can be isolated from both plasma and peritoneal fluid, with peritoneal fluid preferred as the liquid biopsy of choice with higher mutation detection rates. Concordance rates between tissue and plasma/peritoneal ctDNA mutation detection can vary, but is generally high. ctDNA has a potential role in monitoring anti-EGFR treatment response and resistance, as well as in predicting future prognosis and recurrence. The detection of ctDNA in plasma of patients with isolated PM is also possibly suggestive of occult systemic disease, and patients exhibiting such ctDNA positivity may benefit from systemic treatment. Limitations to ctDNA mutation detection may include the size of peritoneal lesions, as well as the fact that PM poorly shed ctDNA. CONCLUSION: While these findings are promising, further large-scale studies are needed to better evaluate the utility of ctDNA in this subset of patients.

Risk factors and early markers for echovirus type 11 associated haemorrhage-hepatitis syndrome in neonates, a retrospective cohort study.

Background: Echovirus type 11(E-11) can cause fatal haemorrhage-hepatitis syndrome in neonates. This study aims to investigate clinical risk factors and early markers of E-11 associated neonatal haemorrhage-hepatitis syndrome. Methods: This is a multicentre retrospective cohort study of 105 neonates with E-11 infection in China. Patients with haemorrhage-hepatitis syndrome (the severe group) were compared with those with mild disease. Clinical risk factors and early markers of haemorrhage-hepatitis syndrome were analysed. In addition, cytokine analysis were performed in selective patients to explore the immune responses. Results: In addition to prematurity, low birth weight, premature rupture of fetal membrane, total parenteral nutrition (PN) (OR, 28.7; 95% CI, 2.8-295.1) and partial PN (OR, 12.9; 95% CI, 2.2-77.5) prior to the onset of disease were identified as risk factors of developing haemorrhage-hepatitis syndrome. Progressive decrease in haemoglobin levels (per 10 g/L; OR, 1.5; 95% CI, 1.1-2.0) and platelet (PLT) < 140 × 109/L at early stage of illness (OR, 17.7; 95% CI, 1.4-221.5) were associated with the development of haemorrhage-hepatitis syndrome. Immunological workup revealed significantly increased interferon-inducible protein-10(IP-10) (P < 0.0005) but decreased IFN-α (P < 0.05) in peripheral blood in severe patients compared with the mild cases. Conclusions: PN may potentiate the development of E-11 associated haemorrhage-hepatitis syndrome. Early onset of thrombocytopenia and decreased haemoglobin could be helpful in early identification of neonates with the disease. The low level of IFN-α and elevated expression of IP-10 may promote the progression of haemorrhage-hepatitis syndrome.

Protein Structure Prediction: Challenges, Advances, and the Shift of Research Paradigms.

Protein structure prediction is an interdisciplinary research topic that has attracted researchers from multiple fields, including biochemistry, medicine, physics, mathematics, and computer science. These researchers adopt various research paradigms to attack the same structure prediction problem: biochemists and physicists attempt to reveal the principles governing protein folding; mathematicians, especially statisticians, usually start from assuming a probability distribution of protein structures given a target sequence and then find the most likely structure, while computer scientists formulate protein structure prediction as an optimization problem - finding the structural conformation with the lowest energy or minimizing the difference between predicted structure and native structure. These research paradigms fall into the two statistical modeling cultures proposed by Leo Breiman, namely, data modeling and algorithmic modeling. Recently, we have also witnessed the great success of deep learning in protein structure prediction. In this review, we present a survey of the efforts for protein structure prediction. We compare the research paradigms adopted by researchers from different fields, with an emphasis on the shift of research paradigms in the era of deep learning. In short, the algorithmic modeling techniques, especially deep neural networks, have considerably improved the accuracy of protein structure prediction; however, theories interpreting the neural networks and knowledge on protein folding are still highly desired.

Anastomotic Leak in Colorectal Surgery: Predictive Factors and Survival.

<br><b>Introduction:</b> Anastomotic leak (AL) is a serious complication following colorectal surgery.</br> <br><b>Aim:</b> The aim of this study was to identify factors associated with the development of AL and to analyze its impact on survival.</br> <br><b>Materials and methods:</b> All consecutive adult colorectal cancer resections performed between 2007 and 2020 with curative intent and anastomosis formation were included from a prospectively maintained database. The primary outcome measure was the rate of AL. The secondary outcome measure was 5-year overall survival (OS).</br> <br><b>Results:</b> There were 6837 eligible patients. The rate of AL was 2.2% and 4.0% in patients with colon and rectal cancer, respectively. AL was a significant independent predictor of reduced 5-year OS in patients who underwent curative surgery for rectal cancer (odds ratio 2.293, p = 0.009). Emergency surgery (p = 0.015), surgery at a public hospital (p = 0.002), and an open surgical approach (p = 0.021) were all associated with a significantly higher risk of AL in patients with colon cancer, with higher rates of AL noted in left colectomies as compared to right hemicolectomies (4.4% <i>vs.</i> 1.3%, p < 0.001). In rectal cancer patients, AL was associated with neoadjuvant chemoradiotherapy (p = 0.038) and male gender (p = 0.002). The anastomosis formation technique (hand-sewn <i>vs.</i> stapled) did not impact the rate of AL (p = 0.116 and p = 0.198 with colon and rectal cancer, respectively).</br> <br><b>Discussion:</b> Clinicians should be cognizant of the predictive factors for AL and should consider early intervention for at-risk patients.</br>.

Predictive Factors for Anastomotic Leak in Colorectal Surgery: A Bi-National Database Study.

IntroductionAnastomotic leak (AL) is a serious complication following colorectal surgery. This study aimed to identify factors associated with the development of AL and analyze its impact on survival.Materials and MethodsAll consecutive adult colorectal cancer resections with curative intent and anastomosis formation were included from a prospectively maintained bi-national database between 2007 and 2020. The primary outcome measure was the rate of AL. The secondary outcome measure was 5-year overall survival (OS).ResultsThere were 7566 eligible patients. The rate of AL was 2.3% and 4.4% in patients with colon and rectal cancer respectively. AL was a significant independent predictor of reduced 5-year OS in patients who underwent curative surgery for rectal cancer (Odds ratio 1.999, p = 0.017). Emergency surgery (p = 0.013), surgery at a public hospital (p < 0.01), and an open surgical approach (p = 0.002) were all significantly associated with a higher risk of AL in patients with colon cancer, with higher rates of AL noted in left colectomies as compared to right hemicolectomies (6.8% vs 1.6%, p < 0.05). In rectal cancer patients, ultra-low anterior resections had the highest risk of AL (4.6%), and associations were found with neoadjuvant chemotherapy (p = 0.011), surgery in a public hospital (p = 0.019), and an open approach (p = 0.035). Anastomosis formation technique (hand-sewn vs stapled) did not impact on rate of AL.DiscussionClinicians should be cognizant of the predictive factors for AL and consider early intervention for patients at risk of this.

ProALIGN: Directly Learning Alignments for Protein Structure Prediction via Exploiting Context-Specific Alignment Motifs.

Template-based modeling (TBM), including homology modeling and protein threading, is one of the most reliable techniques for protein structure prediction. It predicts protein structure by building an alignment between the query sequence under prediction and the templates with solved structures. However, it is still very challenging to build the optimal sequence-template alignment, especially when only distantly related templates are available. Here we report a novel deep learning approach ProALIGN that can predict much more accurate sequence-template alignment. Like protein sequences consisting of sequence motifs, protein alignments are also composed of frequently occurring alignment motifs with characteristic patterns. Alignment motifs are context-specific as their characteristic patterns are tightly related to sequence contexts of the aligned regions. Inspired by this observation, we represent a protein alignment as a binary matrix (in which 1 denotes an aligned residue pair) and then use a deep convolutional neural network to predict the optimal alignment from the query protein and its template. The trained neural network implicitly but effectively encodes an alignment scoring function, which reduces inaccuracies in the handcrafted scoring functions widely used by the current threading approaches. For a query protein and a template, we apply the neural network to directly infer likelihoods of all possible residue pairs in their entirety, which could effectively consider the correlations among multiple residues. We further construct the alignment with maximum likelihood, and finally build a structure model according to the alignment. Tested on three independent data sets with a total of 6688 protein alignment targets and 80 CASP13 TBM targets, our method achieved much better alignments and 3D structure models than the existing methods, including HHpred, CNFpred, CEthreader, and DeepThreader. These results clearly demonstrate the effectiveness of exploiting the context-specific alignment motifs by deep learning for protein threading.