DeepHomo2.0: improved protein-protein contact prediction of homodimers by transformer-enhanced deep learning.

Protein-protein interactions play an important role in many biological processes. However, although structure prediction for monomer proteins has achieved great progress with the advent of advanced deep learning algorithms like AlphaFold, the structure prediction for protein-protein complexes remains an open question. Taking advantage of the Transformer model of ESM-MSA, we have developed a deep learning-based model, named DeepHomo2.0, to predict protein-protein interactions of homodimeric complexes by leveraging the direct-coupling analysis (DCA) and Transformer features of sequences and the structure features of monomers. DeepHomo2.0 was extensively evaluated on diverse test sets and compared with eight state-of-the-art methods including protein language model-based, DCA-based and machine learning-based methods. It was shown that DeepHomo2.0 achieved a high precision of >70% with experimental monomer structures and >60% with predicted monomer structures for the top 10 predicted contacts on the test sets and outperformed the other eight methods. Moreover, even the version without using structure information, named DeepHomoSeq, still achieved a good precision of >55% for the top 10 predicted contacts. Integrating the predicted contacts into protein docking significantly improved the structure prediction of realistic Critical Assessment of Protein Structure Prediction homodimeric complexes. DeepHomo2.0 and DeepHomoSeq are available at http://huanglab.phys.hust.edu.cn/DeepHomo2/.

Integrated proteomic, phosphoproteomic, and N-glycoproteomic analyses of small extracellular vesicles from C2C12 myoblasts identify specific PTM patterns in ligand-receptor interactions.

Small extracellular vesicles (sEVs) are important mediators of intercellular communication by transferring of functional components (proteins, RNAs, and lipids) to recipient cells. Some PTMs, including phosphorylation and N-glycosylation, have been reported to play important role in EV biology, such as biogenesis, protein sorting and uptake of sEVs. MS-based proteomic technology has been applied to identify proteins and PTM modifications in sEVs. Previous proteomic studies of sEVs from C2C12 myoblasts, an important skeletal muscle cell line, focused on identification of proteins, but no PTM information on sEVs proteins is available.In this study, we systematically analyzed the proteome, phosphoproteome, and N-glycoproteome of sEVs from C2C12 myoblasts with LC-MS/MS. In-depth analyses of the three proteomic datasets revealed that the three proteomes identified different catalogues of proteins, and PTMomic analysis could expand the identification of cargos in sEVs. At the proteomic level, a high percentage of membrane proteins, especially tetraspanins, was identified. The sEVs-derived phosphoproteome had a remarkably high level of tyrosine-phosphorylated sites. The tyrosine-phosphorylated proteins might be involved with EPH-Ephrin signaling pathway. At the level of N-glycoproteomics, several glycoforms, such as complex N-linked glycans and sialic acids on glycans, were enriched in sEVs. Retrieving of the ligand-receptor interaction in sEVs revealed that extracellular matrix (ECM) and cell adhesion molecule (CAM) represented the most abundant ligand-receptor pairs in sEVs. Mapping the PTM information on the ligands and receptors revealed that N-glycosylation mainly occurred on ECM and CAM proteins, while phosphorylation occurred on different categories of receptors and ligands. A comprehensive PTM map of ECM-receptor interaction and their components is also provided.In summary, we conducted a comprehensive proteomic and PTMomic analysis of sEVs of C2C12 myoblasts. Integrated proteomic, phosphoproteomic, and N-glycoproteomic analysis of sEVs might provide some insights about their specific uptake mechanism.

Association of dietary inflammatory index and the SARS-CoV-2 infection incidence, severity and mortality of COVID-19: a systematic review and dose-response meta-analysis.

BACKGROUND: Several studies have reported the association between dietary inflammatory index (DII) and the SARS-CoV-2 infection risk, severity or mortality of COVID-19, however, the outcomes remain controversial. OBJECTIVE: We sought to examine whether a dose-response association of DII and SARS-CoV-2 infection exists. DESIGN: A dose-response meta-analysis was performed to investigate the association of DII and SARS-CoV-2 infection. We conducted a systematic search of PubMed, Embase and Web of Science up to March 15th, 2023. The odds ratios (OR) of DII and COVID-19 risk and severity were computed. RESULTS: Totally, 5 studies were included (1 from UK and 4 from Iran), consisting of 197,929 participants with 12,081 COVID-19 cases. Although there was heterogeneity among studies, the results indicated that higher DII was independently related to higher SARS-CoV-2 infection incidence (OR = 1.57, 95% CI: 1.14, 2.17) and COVID-19 severity (OR = 1.11, 95% CI: 1.07, 1.15) but not COVID-19 mortality (risk ratio = 1.13, 95% CI: 1.00, 1.27). The incidence of SARS-CoV-2 infection increased by 31% for each 1-point increase in the E-DII (OR = 1.31, 95% CI: 1.20, 1.43). CONCLUSIONS: This meta-analysis suggests that an elevated DII score is associated with increased SARS-CoV-2 infectious risk and severity of COVID-19. There were not enough studies on COVID-19 mortality. Further large prospective studies in different countries are warranted to validate our results.

Accurate prediction of inter-protein residue-residue contacts for homo-oligomeric protein complexes.

Protein-protein interactions play a fundamental role in all cellular processes. Therefore, determining the structure of protein-protein complexes is crucial to understand their molecular mechanisms and develop drugs targeting the protein-protein interactions. Recently, deep learning has led to a breakthrough in intra-protein contact prediction, achieving an unusual high accuracy in recent Critical Assessment of protein Structure Prediction (CASP) structure prediction challenges. However, due to the limited number of known homologous protein-protein interactions and the challenge to generate joint multiple sequence alignments of two interacting proteins, the advances in inter-protein contact prediction remain limited. Here, we have proposed a deep learning model to predict inter-protein residue-residue contacts across homo-oligomeric protein interfaces, named as DeepHomo. Unlike previous deep learning approaches, we integrated intra-protein distance map and inter-protein docking pattern, in addition to evolutionary coupling, sequence conservation, and physico-chemical information of monomers. DeepHomo was extensively tested on both experimentally determined structures and realistic CASP-Critical Assessment of Predicted Interaction (CAPRI) targets. It was shown that DeepHomo achieved a high precision of >60% for the top predicted contact and outperformed state-of-the-art direct-coupling analysis and machine learning-based approaches. Integrating predicted inter-chain contacts into protein-protein docking significantly improved the docking accuracy on the benchmark dataset of realistic homo-dimeric targets from CASP-CAPRI experiments. DeepHomo is available at http://huanglab.phys.hust.edu.cn/DeepHomo/.

Tumor microenvironment-responsive DNA-based nanomedicine triggers innate sensing for enhanced immunotherapy.

Lack of proper innate sensing inside the tumor microenvironment could reduce both innate and adaptive immunity, which remains a critical cause of immunotherapy failure in various tumor treatments. Double-stranded DNA (dsDNA) has been evidenced to be a promising immunostimulatory agent to induce type I interferons (IFN-Is) production for innate immunity activation through the cyclic GMP-AMP synthase (cGAS)-stimulator of interferon genes (STING) signaling pathway, yet the unsatisfactory delivery and susceptibility to nuclease degradation hindered its feasibility for further clinical applications. Herein, we report on the constructed tumor microenvironment-responsive DNA-based nanomedicine loaded by dendritic mesoporous organosilica nanoparticles (DMONs), which provide efficient delivery of dsDNA to induce intratumoral IFN-Is production for triggering innate sensing for enhanced anti-tumor immunotherapy. Extensive in vitro and in vivo evaluations have demonstrated the dramatic IFN-Is production induced by dsDNA@DMONs in both immune cells and tumor cells, which facilitates dendritic cells (DCs) maturation and T cells activation for eliciting the potent innate immune and adaptive immune responses. Desirable biosafety and marked therapeutic efficacy with a tumor growth inhibition (TGI) of 51.0% on the murine B16-F10 melanoma model were achieved by the single agent dsDNA@DMONs. Moreover, dsDNA@DMONs combined with anti-PD-L1 antibody further enhanced the anti-tumor efficacy and led to almost complete tumor regression. Therefore, this work highlighted the immunostimulatory DNA-based nanomedicine as a promising strategy for overcoming the resistance to immunotherapy, by promoting the IFN-Is production for innate immunity activation and remodeling the tumor microenvironment.

Association of dietary patterns with general and central obesity among Chinese adults: a longitudinal population-based study.

BACKGROUND: Limited evidence exists for the association between dietary patterns and later obesity phenotypes among Chinese adults. This longitudinal study aimed to evaluate associations of dietary patterns with general and central obesity in Chinese adults. METHODS: Based on the China Health and Nutrition Survey (CHNS) waves 2004 and 2015, the study was conducted on 4207 adult men and women (age range: 18-65 years). Dietary intakes were assessed by three consecutive 24-h dietary recalls, and dietary patterns were identified using exploratory factor analysis. Longitudinal associations of dietary patterns with general and central obesity were evaluated using logistic regression analyses. RESULTS: The prevalence rates of general and central obesity were 14.2% and 42.1%, respectively. Factor analysis extracted three major dietary patterns: "traditional southern," "modern," and "traditional northern." After adjustment for potential confounders, adults in the highest quartile of the traditional southern dietary group were less likely to develop over 10 years general (odds ratio [OR] = 0.50, 95% confidence interval [95%CI]: 0.39, 0.65) and central (OR = 0.52, 95%CI: 0.43, 0.63) obesity compared to those in the lowest quartile group. The modern dietary pattern was not significantly associated with general and central obesity. Adherence to the traditional northern dietary pattern increased the chance of both general and central obesity (OR = 1.61, 95%CI: 1.23, 2.10; OR = 1.64, 95%CI: 1.36, 1.98) after 10 years. CONCLUSIONS: Our study provides longitudinal evidence for associations between dietary patterns and later obesity phenotypes among Chinese adults. Our findings may guide the development of evidence-based preventive nutrition interventions to control the obesity epidemic.

Urbanization alters soil bacterial communities in southern China coastal cities.

Urbanization carries essential influences to ecosystem of soil bacteria in coastal cities. Comprehending the patterns and drivers of bacterial diversity are essential to understanding how soil ecosystems respond to environmental change. This study aimed to explore how soil bacterial community (SBC) response to distinct urbanization of coastal cities on composition, assembly process and potential function in Guangdong province, south China. 72 samples from 24 sample sites within 3 cities were included in the study. Soil chemical properties were analyzed, and the bacterial community were investigated by high-throughout sequencing. Proteobacteria and Acidobacteria were the main phyla. Assembly processes remained in stochastic processes and co-occurrence network of SBC kept stable, while urbanization altered SBC by influencing the dominant phyla. The indicators of communities in coastal city soils were the genera gamma_proteobacterium and beta_proteobacterium. Urbanized extent was the non-negligible factor which affected soil bacterial community, despite the total carbon was still the most vital. The impact of urbanization on bacterial communities might follow a non-linear pattern. Faprotax function prediction showed different urbanized coastal city soils share similar metabolic potential. Our study improved our understanding of the response of soil bacterial communities to urbanization in subtropical coastal cities and offered a useful strategy to monitor the ecology risk toward the soil under urbanization.

Inclusion of Desolvation Energy into Protein-Protein Docking through Atomic Contact Potentials.

Protein-protein interactions are crucial in many biological processes. Therefore, determining the structure of a protein-protein complex is valuable for understanding its molecular mechanisms and developing drugs. Molecular docking is a powerful computational tool in the prediction of protein-protein complex structures, in which a scoring function with good performance is very important. In this study, we have proposed a hybrid scoring function of atomic contact-based desolvation energies and distance-dependent interatomic potentials for protein-protein interactions, named HITScorePP, where the atomic contact desolvation energies were derived using an iterative method and the distance-dependent potentials were directly taken from our ITScorePP scoring function. Integrating the hybrid scoring function into our fast Fourier transform (FFT) based HDOCK docking scheme, the updated docking program, named HDOCK2.0, significantly improved the docking performance on the 55 newly added complexes in the protein docking benchmark 5.0 and a data set of 19 antibacterial protein complexes. HDOCK2.0 was also compared with four other state-of-the-art docking programs including Rosetta, ZDOCK3.0.2, FRODOCK3.0, ATTRACT, and PatchDock and obtained the overall best performance in binding mode predictions. These results demonstrated the accuracy of our hybrid scoring function and the necessity of included desolvation effects in protein-protein docking.

[Systematic review and Meta-analysis of Lianhua Qingwen preparations combined with Oseltamivir in treatment of influenza].

This study aims to explore the efficacy and safety of Lianhua Qingwen preparations combined with Oseltamivir in the treatment of influenza patients. PubMed, Cochrane Library, EMbase, SinoMed, CNKI, Wanfang, and VIP were searched for the randomized controlled trials(RCTs) involving the comparison between the influenza patients treated with Lianhua Qingwen preparations combined with Oseltamivir and those treated with Oseltamivir alone. Fever clearance time was taken as the primary outcome indicator. Clinical effective rate(markedly effective and effective), time to muscle pain relief, time to sore throat relief, time to cough relief, time to nasal congestion and runny nose relief, time to negative result of viral nucleic acid test, and adverse reactions were taken as the secondary outcome indicators. The data were extracted based on the outcome indicators and then combined. The Cochrane collaboration's tool for assessing risk of bias was used to evaluate the quality of a single RCT, and the grading of recommendations assessment, development and evaluations(GRADE) system to assess the quality of a single outcome indicator. RevMan 5.3 was employed to analyze data and test heterogeneity. Finally, 16 RCTs involving 1 629 patients were included for analysis. The Meta-analysis showed that Lianhua Qingwen preparations combined with Oseltamivir was superior to Oseltamivir alone in the treatment of influenza in terms of clinical effective rate(RR=1.16, 95%CI [1.12, 1.20], P<0.000 01), fever clearance time(SMD=-2.02, 95%CI [-2.62,-1.41], P<0.000 01), time to muscle pain relief(SMD=-2.50, 95%CI [-3.84,-1.16], P=0.000 2), time to sore throat relief(SMD=-1.40, 95%CI [-1.93,-0.85], P<0.000 01), time to cough relief(SMD=-1.81, 95%CI [-2.44,-1.19], P<0.000 01), time to nasal congestion and runny nose(SMD=-2.31, 95%CI [-3.61,-1.01], P=0.000 5), and time to negative result of viral nucleic acid test(SMD=-0.68, 95%CI [-1.19,-0.16], P=0.01). However, due to the low quality of the trials, the above conclusions need to be proved by more high-quality clinical studies. In addition, we still need to attach importance to the adverse reactions of the integrated application of Chinese and western medicines.

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.

HSYMDOCK: a docking web server for predicting the structure of protein homo-oligomers with Cn or Dn symmetry.

A major subclass of protein-protein interactions is formed by homo-oligomers with certain symmetry. Therefore, computational modeling of the symmetric protein complexes is important for understanding the molecular mechanism of related biological processes. Although several symmetric docking algorithms have been developed for Cn symmetry, few docking servers have been proposed for Dn symmetry. Here, we present HSYMDOCK, a web server of our hierarchical symmetric docking algorithm that supports both Cn and Dn symmetry. The HSYMDOCK server was extensively evaluated on three benchmarks of symmetric protein complexes, including the 20 CASP11-CAPRI30 homo-oligomer targets, the symmetric docking benchmark of 213 Cn targets and 35 Dn targets, and a nonredundant test set of 55 transmembrane proteins. It was shown that HSYMDOCK obtained a significantly better performance than other similar docking algorithms. The server supports both sequence and structure inputs for the monomer/subunit. Users have an option to provide the symmetry type of the complex, or the server can predict the symmetry type automatically. The docking process is fast and on average consumes 10∼20 min for a docking job. The HSYMDOCK web server is available at http://huanglab.phys.hust.edu.cn/hsymdock/.

Determination of an effective scoring function for RNA-RNA interactions with a physics-based double-iterative method.

RNA-RNA interactions play fundamental roles in gene and cell regulation. Therefore, accurate prediction of RNA-RNA interactions is critical to determine their complex structures and understand the molecular mechanism of the interactions. Here, we have developed a physics-based double-iterative strategy to determine the effective potentials for RNA-RNA interactions based on a training set of 97 diverse RNA-RNA complexes. The double-iterative strategy circumvented the reference state problem in knowledge-based scoring functions by updating the potentials through iteration and also overcame the decoy-dependent limitation in previous iterative methods by constructing the decoys iteratively. The derived scoring function, which is referred to as DITScoreRR, was evaluated on an RNA-RNA docking benchmark of 60 test cases and compared with three other scoring functions. It was shown that for bound docking, our scoring function DITScoreRR obtained the excellent success rates of 90% and 98.3% in binding mode predictions when the top 1 and 10 predictions were considered, compared to 63.3% and 71.7% for van der Waals interactions, 45.0% and 65.0% for ITScorePP, and 11.7% and 26.7% for ZDOCK 2.1, respectively. For unbound docking, DITScoreRR achieved the good success rates of 53.3% and 71.7% in binding mode predictions when the top 1 and 10 predictions were considered, compared to 13.3% and 28.3% for van der Waals interactions, 11.7% and 26.7% for our ITScorePP, and 3.3% and 6.7% for ZDOCK 2.1, respectively. DITScoreRR also performed significantly better in ranking decoys and obtained significantly higher score-RMSD correlations than the other three scoring functions. DITScoreRR will be of great value for the prediction and design of RNA structures and RNA-RNA complexes.

[Thoughts and suggestions on arrangement, analysis and summary of medical data during COVID-19 epidemic].

The analysis and utilization of clinical scientific research data is an effective means to promote the progress of diagnosis and treatment, and a key step in the development of medical sciences. During the epidemic of coronavirus disease 2019(COVID-19), how to transform the limited diagnostic data into clinical research resources has attracted much attention. Based on the low efficiency of data collection and extraction, the inconsistency of data analysis, the irregularity of data report and the high timeliness of data update during the epidemic, this paper briefly analyzed the background and reasons of data application under the current situation, and then discusses the problems and feasible solutions of clinical data applications under the epidemic situation and, more importantly, for future medical clinical research methods. We put forward several methodological suggestions: ① gradually improve the medical big data model and establish the national medical health data center; ② improve the scientific research literacy of medical staff and popularize the basic skills and knowledge of GCP; ③ promote a scientific, networked and shared data collection and management mode; ④ use the mixed research method and collective analysis to improve the efficiency of clinical data analysis; ⑤ pay attention to narration of the medical feelings and emphasize the humanistic data of clinical medicine. It is expected to promote the standardized and reasonable use of clinical scientific research data, the rigorous integration of expert opinions, and ultimately the development of big data for national health care.

[Thoughts and suggestions on analysis of death cases report during COVID-19 epidemic].

It is an essential task to discuss the death cases for clinicians. During the emergent public events, the report and analysis of death cases is of far-reaching significance. The epidemic of coronavirus disease 2019(COVID-19) has brought huge losses to China, and the medical system has been sustaining tremendous pressure. The best weapon to defeat the epidemic is medical data and related scientific research, of which the systematic analysis and efficient use of death cases is a key step. Based on the incomplete record of death case report, the lack of humanistic perspective and patient report, every department and institution is facing great challenge in terms of data management. Given that the relevant systems need to be improved, and that the integration of standardized reports and clinical research is not mature,as well as other problems, we put forward several methodological suggestions: ① Establish national medical and health data center and improve relevant laws and regulations. ② Increase investment in medical data management and start data collection and analysis as early as possible during the epidemic. ③ Refine the content of death case report and promote the standardization of report. ④ Pay close attention to the report of death cases, review, summary and analysis. More importantly, we should continue to build and improve platforms and programs related to disease control, carry out epidemic-associated scientific research, enhance the managing efficiency of public health data, elevate the anti-risk capability of our medical system, and promote the steady progress of the health China strategy.

Pushing the accuracy limit of shape complementarity for protein-protein docking.

BACKGROUND: Protein-protein docking is a valuable computational approach for investigating protein-protein interactions. Shape complementarity is the most basic component of a scoring function and plays an important role in protein-protein docking. Despite significant progresses, shape representation remains an open question in the development of protein-protein docking algorithms, especially for grid-based docking approaches. RESULTS: We have proposed a new pairwise shape-based scoring function (LSC) for protein-protein docking which adopts an exponential form to take into account long-range interactions between protein atoms. The LSC scoring function was incorporated into our FFT-based docking program and evaluated for both bound and unbound docking on the protein docking benchmark 4.0. It was shown that our LSC achieved a significantly better performance than four other similar docking methods, ZDOCK 2.1, MolFit/G, GRAMM, and FTDock/G, in both success rate and number of hits. When considering the top 10 predictions, LSC obtained a success rate of 51.71% and 6.82% for bound and unbound docking, respectively, compared to 42.61% and 4.55% for the second-best program ZDOCK 2.1. LSC also yielded an average of 8.38 and 3.94 hits per complex in the top 1000 predictions for bound and unbound docking, respectively, followed by 6.38 and 2.96 hits for the second-best ZDOCK 2.1. CONCLUSIONS: The present LSC method will not only provide an initial-stage docking approach for post-docking processes but also have a general implementation for accurate representation of other energy terms on grids in protein-protein docking. The software has been implemented in our HDOCK web server at http://hdock.phys.hust.edu.cn/.

Blind prediction of homo- and hetero-protein complexes: The CASP13-CAPRI experiment.

We present the results for CAPRI Round 46, the third joint CASP-CAPRI protein assembly prediction challenge. The Round comprised a total of 20 targets including 14 homo-oligomers and 6 heterocomplexes. Eight of the homo-oligomer targets and one heterodimer comprised proteins that could be readily modeled using templates from the Protein Data Bank, often available for the full assembly. The remaining 11 targets comprised 5 homodimers, 3 heterodimers, and two higher-order assemblies. These were more difficult to model, as their prediction mainly involved "ab-initio" docking of subunit models derived from distantly related templates. A total of ~30 CAPRI groups, including 9 automatic servers, submitted on average ~2000 models per target. About 17 groups participated in the CAPRI scoring rounds, offered for most targets, submitting ~170 models per target. The prediction performance, measured by the fraction of models of acceptable quality or higher submitted across all predictors groups, was very good to excellent for the nine easy targets. Poorer performance was achieved by predictors for the 11 difficult targets, with medium and high quality models submitted for only 3 of these targets. A similar performance "gap" was displayed by scorer groups, highlighting yet again the unmet challenge of modeling the conformational changes of the protein components that occur upon binding or that must be accounted for in template-based modeling. Our analysis also indicates that residues in binding interfaces were less well predicted in this set of targets than in previous Rounds, providing useful insights for directions of future improvements.

MiR-155 expressed in bone marrow-derived lymphocytes promoted lipopolysaccharide-induced acute lung injury through Ang-2-Tie-2 pathway.

Acute lung injury (ALI) is a type of diffuse lung inflammation with a high mortality rate. Studies show that miR-155 plays an important role in inflammation. Here, we investigated the role of miR-155 in lipopolysaccharide (LPS)-induced ALI. The mice with bone marrow transplantation between MiR-155 knockout and wild-type were used as animal models of LPS-induced sepsis. In response to LPS injection, ALI was less severe in miR-155 knockout mice than in wild-type mice, and mainly manifested as reduced pulmonary vascular leakage, pulmonary edema, and neutrophil infiltration. The expression levels of Ang-2 and apoptosis-associated caspases-3 and -9, as well as myosin light chain (MLC) phosphorylation in the lungs were also decreased. A bone marrow transplantation experiment showed that miR-155 expressed in bone marrow-derived lymphocytes rather than lung parenchymal lymphocytes promoted inflammation. Findings suggest that miR-155 expressed in bone marrow-derived lymphocytes promoted LPS-induced ALI through the modulation of the Ang-2-Tie-2 pathway.

RRDB: a comprehensive and non-redundant benchmark for RNA-RNA docking and scoring.

Motivation: With the discovery of more and more noncoding RNAs and their versatile functions, RNA-RNA interactions have received increased attention. Therefore, determination of their complex structures is valuable to understand the molecular mechanism of the interactions. Given the high cost of experimental methods, computational approaches like molecular docking have played an important role in the determination of complex structures, in which a benchmark is critical for the development of docking algorithms. Results: Meeting the need, we have developed the first comprehensive and nonredundant RNA-RNA docking benchmark (RRDB). The diverse dataset of 123 targets consists of 78 unbound-unbound and 45 bound-unbound (or unbound-bound) test cases. The dataset was classified into three groups according to the interface conformational changes between bound and unbound structures: 47 'easy', 38 'medium' and 38 'difficult' targets. A docking test with the benchmark using ZDOCK 2.1 demonstrated the challenging nature of the RNA-RNA docking problem and the important value of the present benchmark. The bound and unbound cases of the benchmark will be beneficial for the development and optimization of docking and scoring algorithms for RNA-RNA interactions. Availability and implementation: The benchmark is available at http://huanglab.phys.hust.edu.cn/RRDbenchmark/. Contact: huangsy@hust.edu.cn. Supplementary information: Supplementary data are available at Bioinformatics online.

HDOCK: a web server for protein-protein and protein-DNA/RNA docking based on a hybrid strategy.

Protein-protein and protein-DNA/RNA interactions play a fundamental role in a variety of biological processes. Determining the complex structures of these interactions is valuable, in which molecular docking has played an important role. To automatically make use of the binding information from the PDB in docking, here we have presented HDOCK, a novel web server of our hybrid docking algorithm of template-based modeling and free docking, in which cases with misleading templates can be rescued by the free docking protocol. The server supports protein-protein and protein-DNA/RNA docking and accepts both sequence and structure inputs for proteins. The docking process is fast and consumes about 10-20 min for a docking run. Tested on the cases with weakly homologous complexes of <30% sequence identity from five docking benchmarks, the HDOCK pipeline tied with template-based modeling on the protein-protein and protein-DNA benchmarks and performed better than template-based modeling on the three protein-RNA benchmarks when the top 10 predictions were considered. The performance of HDOCK became better when more predictions were considered. Combining the results of HDOCK and template-based modeling by ranking first of the template-based model further improved the predictive power of the server. The HDOCK web server is available at http://hdock.phys.hust.edu.cn/.

Hierarchical Flexible Peptide Docking by Conformer Generation and Ensemble Docking of Peptides.

Given the importance of peptide-mediated protein interactions in cellular processes, protein-peptide docking has received increasing attention. Here, we have developed a Hierarchical flexible Peptide Docking approach through fast generation and ensemble docking of peptide conformations, which is referred to as HPepDock. Tested on the LEADS-PEP benchmark data set of 53 diverse complexes with peptides of 3-12 residues, HPepDock performed significantly better than the 11 docking protocols of five small-molecule docking programs (DOCK, AutoDock, AutoDock Vina, Surflex, and GOLD) in predicting near-native binding conformations. HPepDock was also evaluated on the 19 bound/unbound and 10 unbound/unbound protein-peptide complexes of the Glide SP-PEP benchmark and showed an overall better performance than Glide SP-PEP+MM-GBSA and FlexPepDock in both bound and unbound docking. HPepDock is computationally efficient, and the average running time for docking a peptide is ∼15 min with the range from about 1 min for short peptides to around 40 min for long peptides.