Addressing recent docking challenges: A hybrid strategy to integrate template-based and free protein-protein docking

Interaction of serum proteins with SARS-CoV-2 RBD

The outbreak of the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has become a worldwide public health crisis. When the SARS-CoV-2 enters the biological fluids in the human body, different types of biomolecules (in particular proteins) may adsorb on its surface and alter its infection ability. Although great efforts have recently been devoted to the interaction of specific antibodies with the SARS-CoV-2, it still remains largely unknown how the other serum proteins affect the infection of the SARS-CoV-2. In this work, we systematically investigate the interaction of serum proteins with the SARS-CoV-2 RBD by molecular docking and all-atom molecular dynamics simulations. It is found that non-specific immunoglobulins (Ig) indeed cannot effectively bind to the SARS-CoV-2 RBD while human serum albumin (HSA) may have some potential in blocking its infection (to ACE2). More importantly, we find that the RBD can cause significant structural changes in Apolipoprotein E (ApoE), by which SARS-CoV-2 may hijack the metabolic pathway of ApoE to facilitate its cell entry. The present study enhances the understanding of the role of protein corona in the bio-behaviors of SARS-CoV-2, which may aid the more precise and personalized treatment for COVID-19 infection in the clinic.

In Silico Design of Peptide-Based SARS-CoV-2 Fusion Inhibitors That Target WT and Mutant Versions of SARS-CoV-2 HR1 Domains

In 2019, novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) began infecting humans, resulting in the COVID-19 pandemic. While the push for development of vaccines has yielded some positive results, the emergence of additional variants has led to concerns surrounding sustained vaccine effectiveness as the variants become the dominant strains. This work was undertaken to develop peptide-based antivirals capable of targeting both the wildtype (WT) heptad repeat 1 (HR1) domain of SARS-CoV-2 and the new HR1 variants which have developed. In silico protein mutagenesis, structural characterization, and protein–protein molecular docking were utilized to determine molecular interactions which facilitated binding of peptide-based antivirals targeting the HR1 domains. Molecular dynamics simulations were utilized to predict the final binding affinities of the top five peptide inhibitors designed. This work demonstrated the importance of hydrophobic interactions in the hydrophobic gorge and in the rim of the HR1 domain. Additionally, the placement of charged residues was shown to be essential in maximizing electrostatic interactions. The top five designed peptide inhibitors were all demonstrated to maintain good binding affinity to the WT and the variant HR1 SARS-CoV-2 domains. Therefore, the peptide inhibitors designed in this work could serve as potent antivirals which are effective in targeting both the original SARS-CoV-2 and the HR1 variants that have developed.

In silico Design of Linear DNA for Robust Cell-Free Gene Expression

Cell-free gene expression systems with linear DNA expression templates (LDETs) have been widely applied in artificial cells, biochips, and high-throughput screening. However, due to the degradation caused by native nucleases in cell extracts, the transcription with linear DNA templates is weak, thereby resulting in low protein expression level, which greatly limits the development of cell-free systems using linear DNA templates. In this study, the protective sequences for stabilizing linear DNA and the transcribed mRNAs were rationally designed according to nucleases' action mechanism, whose effectiveness was evaluated through computer simulation and cell-free gene expression. The cell-free experiment results indicated that, with the combined protection of designed sequence and GamS protein, the protein expression of LDET-based cell-free systems could reach the same level as plasmid-based cell-free systems. This study would potentially promote the development of the LDET-based cell-free gene expression system for broader applications.

Identifying potential drug targets and candidate drugs for COVID-19: biological networks and structural modeling approaches

Background: Coronavirus (CoV) is an emerging human pathogen causing severe acute respiratory syndrome (SARS) around the world. Earlier identification of biomarkers for SARS can facilitate detection and reduce the mortality rate of the disease. Thus, by integrated network analysis and structural modeling approach, we aimed to explore the potential drug targets and the candidate drugs for coronavirus medicated SARS. Methods: Differentially expression (DE) analysis of CoV infected host genes (HGs) expression profiles was conducted by using the Limma. Highly integrated DE-CoV-HGs were selected to construct the protein-protein interaction (PPI) network.  Results: Using the Walktrap algorithm highly interconnected modules include module 1 (202 nodes); module 2 (126 nodes) and module 3 (121 nodes) modules were retrieved from the PPI network. MYC, HDAC9, NCOA3, CEBPB, VEGFA, BCL3, SMAD3, SMURF1, KLHL12, CBL, ERBB4, and CRKL were identified as potential drug targets (PDTs), which are highly expressed in the human respiratory system after CoV infection. Functional terms growth factor receptor binding, c-type lectin receptor signaling, interleukin-1 mediated signaling, TAP dependent antigen processing and presentation of peptide antigen via MHC class I, stimulatory T cell receptor signaling, and innate immune response signaling pathways, signal transduction and cytokine immune signaling pathways were enriched in the modules. Protein-protein docking results demonstrated the strong binding affinity (-314.57 kcal/mol) of the ERBB4-3cLpro complex which was selected as a drug target. In addition, molecular dynamics simulations indicated the structural stability and flexibility of the ERBB4-3cLpro complex. Further, Wortmannin was proposed as a candidate drug to ERBB4 to control SARS-CoV-2 pathogenesis through inhibit receptor tyrosine kinase-dependent macropinocytosis, MAPK signaling, and NF-kb singling pathways that regulate host cell entry, replication, and modulation of the host immune system. Conclusion: We conclude that CoV drug target “ERBB4” and candidate drug “Wortmannin” provide insights on the possible personalized therapeutics for emerging COVID-19.

Foamy Viruses, Bet, and APOBEC3 Restriction

Non-human primates (NHP) are an important source of viruses that can spillover to humans and, after adaptation, spread through the host population. Whereas HIV-1 and HTLV-1 emerged as retroviral pathogens in humans, a unique class of retroviruses called foamy viruses (FV) with zoonotic potential are occasionally detected in bushmeat hunters or zookeepers. Various FVs are endemic in numerous mammalian natural hosts, such as primates, felines, bovines, and equines, and other animals, but not in humans. They are apathogenic, and significant differences exist between the viral life cycles of FV and other retroviruses. Importantly, FVs replicate in the presence of many well-defined retroviral restriction factors such as TRIM5α, BST2 (Tetherin), MX2, and APOBEC3 (A3). While the interaction of A3s with HIV-1 is well studied, the escape mechanisms of FVs from restriction by A3 is much less explored. Here we review the current knowledge of FV biology, host restriction factors, and FV-host interactions with an emphasis on the consequences of FV regulatory protein Bet binding to A3s and outline crucial open questions for future studies.

Mutational analysis of structural proteins of SARS-CoV-2

SARS-CoV-2 transmissibility is higher than that of other human coronaviruses; therefore, it poses a threat to the populated communities. We investigated mutations among envelope (E), membrane (M), and spike (S) proteins from different isolates of SARS-CoV-2 and plausible signaling influenced by mutated virus in a host. We procured updated protein sequences from the NCBI virus database. Mutations were analyzed in the retrieved sequences of the viral proteins through multiple sequence alignment. Additionally, the data was subjected to ScanPROSITE to analyse if the mutations generated a relevant sequence for host signaling. Unique mutations in E, M, and S proteins resulted in modification sites like PKC phosphorylation and N-myristoylation sites. Based on structural analysis, our study revealed that the D614G mutation in the S protein diminished the interaction with T859 and K854 of adjacent chains. Moreover, the S protein of SARS-CoV-2 consists of an Arg-Gly-Asp (RGD) tripeptide sequence, which could potentially interact with various members of integrin family receptors. RGD sequence in S protein might aid in the initial virus attachment. We speculated crucial host pathways which the mutated isolates of SARS-CoV-2 may alter like PKC, Src, and integrin mediated signaling pathways. PKC signaling is known to influence the caveosome/raft pathway which is critical for virus entry. Additionally, the myristoylated proteins might activate NF-κB, a master molecule of inflammation. Thus the mutations may contribute to the disease pathogenesis and distinct lung pathophysiological changes. Further the frequently occurring mutations in the protein can be studied for possible therapeutic interventions.

SHP2 Nuclear/Cytoplasmic Trafficking in Granulosa Cells Is Essential for Oocyte Meiotic Resumption and Maturation

Src-homology-2-containing phosphotyrosine phosphatase (SHP2), a classic cytoplasmic protein and a major regulator of receptor tyrosine kinases and G protein-coupled receptors, plays a significant role in preimplantation embryo development. In this study, we deciphered the role of SHP2 in the somatic compartment of oocytes during meiotic maturation. SHP2 showed nuclear/cytoplasmic localization in bovine cumulus and human granulosa (COV434) cells. Follicle-stimulating hormone (FSH) treatment significantly enhanced cytoplasmic SHP2 localization, in contrast to the E₂ treatment, which augmented nuclear localization. Enhanced cytoplasmic SHP2 was found to negatively regulate the expression of the ERα-transcribed NPPC and NPR2 mRNAs, which are vital for oocyte meiotic arrest. The co-immunoprecipitation results revealed the presence of the SHP2/ERα complex in the germinal vesicle-stage cumulus-oocyte complexes, and this complex significantly decreased with the progression of meiotic maturation. The complex formation between ERα and SHP2 was also confirmed by using a series of computational modeling methods. To verify the correlation between SHP2 and NPPC/NPR2, SHP2 was knocked down via RNA interference, and NPPC and NPR2 mRNAs were analyzed in the control, E₂, and FSH-stimulated COV434 cells. Furthermore, phenyl hydrazonopyrazolone sulfonate 1, a site-directed inhibitor of active SHP2, showed no significant effect on the ERα-transcribed NPPC and NPR2 mRNAs. Taken together, these findings support a novel nuclear/cytoplasmic role of SHP2 in oocyte meiotic resumption and maturation.

Capsid Structure of Leishmania RNA Virus 1

Twelve million people worldwide suffer from leishmaniasis, resulting in more than 30 thousand deaths annually. The disease has several variants that differ in their symptoms.

Leishmania parasites cause a variety of symptoms, including mucocutaneous leishmaniasis, which results in the destruction of the mucous membranes of the nose, mouth, and throat. The species of Leishmania carrying Leishmania RNA virus 1 (LRV1), from the family Totiviridae, are more likely to cause severe disease and are less sensitive to treatment than those that do not contain the virus. Although the importance of LRV1 for the severity of leishmaniasis was discovered a long time ago, the structure of the virus remained unknown. Here, we present a cryo-electron microscopy reconstruction of the virus-like particle of LRV1 determined to a resolution of 3.65 Å. The capsid has icosahedral symmetry and is formed by 120 copies of a capsid protein assembled in asymmetric dimers. RNA genomes of viruses from the family Totiviridae are synthetized, but not capped at the 5′ end, by virus RNA polymerases. To protect viral RNAs from degradation, capsid proteins of the L-A totivirus cleave the 5′ caps of host mRNAs, creating decoys to overload the cellular RNA quality control system. Capsid proteins of LRV1 form positively charged clefts, which may be the cleavage sites for the 5′ cap of Leishmania mRNAs. The putative RNA binding site of LRV1 is distinct from that of the related L-A virus. The structure of the LRV1 capsid enables the rational design of compounds targeting the putative decapping site. Such inhibitors may be developed into a treatment for mucocutaneous leishmaniasis caused by LRV1-positive species of Leishmania.

IMPORTANCE Twelve million people worldwide suffer from leishmaniasis, resulting in more than 30 thousand deaths annually. The disease has several variants that differ in their symptoms. The mucocutaneous form, which leads to disintegration of the nasal septum, lips, and palate, is caused predominantly by Leishmania parasites carrying Leishmania RNA virus 1 (LRV1). Here, we present the structure of the LRV1 capsid determined using cryo-electron microscopy. Capsid proteins of a related totivirus, L-A virus, protect viral RNAs from degradation by cleaving the 5′ caps of host mRNAs. Capsid proteins of LRV1 may have the same function. We show that the LRV1 capsid contains positively charged clefts that may be sites for the cleavage of mRNAs of Leishmania cells. The structure of the LRV1 capsid enables the rational design of compounds targeting the putative mRNA cleavage site. Such inhibitors may be used as treatments for mucocutaneous leishmaniasis.

Molecular docking simulation reveals ACE2 polymorphisms that may increase the affinity of ACE2 with the SARS-CoV-2 Spike protein

There is increasing evidence that ACE2 gene polymorphism can modulate the interaction between ACE2 and the SARS-CoV-2 spike protein affecting the viral entry into the host cell, and/or contribute to lung and systemic damage in COVID-19. Here we used in silico molecular docking to predict the effects of ACE2 missense variants on the interaction with the spike protein of SARS-CoV-2. HDOCK and FireDock simulations identified 6 ACE2 missense variants (I21T, A25T, K26R, E37K, T55A, E75G) with higher affinity for SARS-CoV-2 Spike protein receptor binding domain (RBD) with respect to wild type ACE2, and 11 variants (I21V, E23K, K26E, T27A, E35K, S43R, Y50F, N51D, N58H, K68E, M82I) with lower affinity. This result supports the hypothesis that ACE2 genetic background may represent the first "genetic gateway" during the disease progression.

Identifying potential drug targets and candidate drugs for COVID-19: biological networks and structural modeling approaches

Background: Coronavirus (CoV) is an emerging human pathogen causing severe acute respiratory syndrome (SARS) around the world. Earlier identification of biomarkers for SARS can facilitate detection and reduce the mortality rate of the disease. Thus, by integrated network analysis and structural modeling approach, we aimed to explore the potential drug targets and the candidate drugs for coronavirus medicated SARS. Methods: Differentially expression (DE) analysis of CoV infected host genes (HGs) expression profiles was conducted by using the Limma. Highly integrated DE-CoV-HGs were selected to construct the protein-protein interaction (PPI) network.  Results: Using the Walktrap algorithm highly interconnected modules include module 1 (202 nodes); module 2 (126 nodes) and module 3 (121 nodes) modules were retrieved from the PPI network. MYC, HDAC9, NCOA3, CEBPB, VEGFA, BCL3, SMAD3, SMURF1, KLHL12, CBL, ERBB4, and CRKL were identified as potential drug targets (PDTs), which are highly expressed in the human respiratory system after CoV infection. Functional terms growth factor receptor binding, c-type lectin receptor signaling, interleukin-1 mediated signaling, TAP dependent antigen processing and presentation of peptide antigen via MHC class I, stimulatory T cell receptor signaling, and innate immune response signaling pathways, signal transduction and cytokine immune signaling pathways were enriched in the modules. Protein-protein docking results demonstrated the strong binding affinity (-314.57 kcal/mol) of the ERBB4-3cLpro complex which was selected as a drug target. In addition, molecular dynamics simulations indicated the structural stability and flexibility of the ERBB4-3cLpro complex. Further, Wortmannin was proposed as a candidate drug to ERBB4 to control SARS-CoV-2 pathogenesis through inhibit receptor tyrosine kinase-dependent macropinocytosis, MAPK signaling, and NF-kb singling pathways that regulate host cell entry, replication, and modulation of the host immune system. Conclusion: We conclude that CoV drug target "ERBB4" and candidate drug "Wortmannin" provide insights on the possible personalized therapeutics for emerging COVID-19.

Identifying potential drug targets and candidate drugs for COVID-19: biological networks and structural modeling approaches

Background: Coronavirus (CoV) is an emerging human pathogen causing severe acute respiratory syndrome (SARS) around the world. Earlier identification of biomarkers for SARS can facilitate detection and reduce the mortality rate of the disease. Thus, by integrated network analysis and structural modeling approach, we aimed to explore the potential drug targets and the candidate drugs for coronavirus medicated SARS. Methods: Differentially expression (DE) analysis of CoV infected host genes (HGs) expression profiles was conducted by using the Limma. Highly integrated DE-CoV-HGs were selected to construct the protein-protein interaction (PPI) network.  Results: Using the Walktrap algorithm highly interconnected modules include module 1 (202 nodes); module 2 (126 nodes) and module 3 (121 nodes) modules were retrieved from the PPI network. MYC, HDAC9, NCOA3, CEBPB, VEGFA, BCL3, SMAD3, SMURF1, KLHL12, CBL, ERBB4, and CRKL were identified as potential drug targets (PDTs), which are highly expressed in the human respiratory system after CoV infection. Functional terms growth factor receptor binding, c-type lectin receptor signaling, interleukin-1 mediated signaling, TAP dependent antigen processing and presentation of peptide antigen via MHC class I, stimulatory T cell receptor signaling, and innate immune response signaling pathways, signal transduction and cytokine immune signaling pathways were enriched in the modules. Protein-protein docking results demonstrated the strong binding affinity (-314.57 kcal/mol) of the ERBB4-3cLpro complex which was selected as a drug target. In addition, molecular dynamics simulations indicated the structural stability and flexibility of the ERBB4-3cLpro complex. Further, Wortmannin was proposed as a candidate drug to ERBB4 to control SARS-CoV-2 pathogenesis through inhibit receptor tyrosine kinase-dependent macropinocytosis, MAPK signaling, and NF-kb singling pathways that regulate host cell entry, replication, and modulation of the host immune system. Conclusion: We conclude that CoV drug target "ERBB4" and candidate drug "Wortmannin" provide insights on the possible personalized therapeutics for emerging COVID-19.

ITScore-NL: An Iterative Knowledge-Based Scoring Function for Nucleic Acid-Ligand Interactions

Nucleic acid-ligand complexes underlie numerous cellular processes, such as gene function expression and regulation, in which their three-dimensional structures are important to understand their functions and thus to develop therapeutic interventions. Given the high cost and technical difficulties in experimental methods, computational methods such as molecular docking have been actively used to investigate nucleic acid-ligand interactions in which an accurate scoring function is crucial. However, because of the limited number of experimental nucleic acid-ligand binding data and structures, the scoring function development for nucleic acid-ligand interactions falls far behind that for protein-protein and protein-ligand interactions. Here, based on our statistical mechanics-based iterative approach, we have developed an iterative knowledge-based scoring function for nucleic acid-ligand interactions, named as ITScore-NL, by explicitly including stacking and electrostatic potentials. Our ITScore-NL scoring function was extensively evaluated for its ability in the binding mode and binding affinity predictions on three diverse test sets and compared with state-of-the-art scoring functions. Overall, ITScore-NL obtained significantly better performance than the other 12 scoring functions and predicted near-native poses with rmsd ≤ 1.5 Å for 71.43% of the cases when the top three binding modes were considered and a good correlation of R = 0.64 in binding affinity prediction on the large test set of 77 nucleic acid-ligand complexes. These results suggested the accuracy of ITScore-NL and the necessity of explicitly including stacking and electrostatic potentials.

Computational structure modeling for diverse categories of macromolecular interactions

Computational protein-protein docking is one of the most intensively studied topics in structural bioinformatics. The field has made substantial progress through over three decades of development. The development began with methods for rigid-body docking of two proteins, which have now been extended in different directions to cover the various macromolecular interactions observed in a cell. Here, we overview the recent developments of the variations of docking methods, including multiple protein docking, peptide-protein docking, and disordered protein docking methods.

Testing amino acid-codon affinity hypothesis using molecular docking

Genetic code refers to a set of rules that assign trinucleotides called codons to amino acids in the process of protein synthesis. Investigating the genetic code's logic and its evolutionary origin has always been both intriguing and challenging. While the correspondence rules between codons and amino acids in the genetic code are well-known, it is still unclear whether those assignments can be explained based on energetic or/and entropic arguments. As an attempt at deciphering basic thermodynamic rules governing DNA translation, we used molecular docking to investigate the ability of amino acids to bind to their corresponding anticodon compared to other codons. The total number of 1280 direct docking interactions have been performed for each amino acid-codon/anti-codon case to find whether the amino acids have a preference to bind to their cognate anticodons or codons. Based on docking scores which are expected to correlate with binding affinity, no correlation with genetic correspondence rules was observed suggesting a more subtle process, other than direct binding, to explain codon-amino-acid specificity.

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

MOTIVATION

Given the importance of protein-ribonucleic acid (RNA) interactions in many biological processes, a variety of docking algorithms have been developed to predict the complex structure from individual protein and RNA partners in the past decade. However, due to the impact of molecular flexibility, the performance of current methods has hit a bottleneck in realistic unbound docking. Pushing the limit, we have proposed a protein-ensemble-RNA docking strategy to explicitly consider the protein flexibility in protein-RNA docking through an ensemble of multiple protein structures, which is referred to as MPRDock. Instead of taking conformations from MD simulations or experimental structures, we obtained the multiple structures of a protein by building models from its homologous templates in the Protein Data Bank (PDB).

RESULTS

Our approach can not only avoid the reliability issue of structures from MD simulations but also circumvent the limited number of experimental structures for a target protein in the PDB. Tested on 68 unbound-bound and 18 unbound-unbound protein-RNA complexes, our MPRDock/DITScorePR considerably improved the docking performance and achieved a significantly higher success rate than single-protein rigid docking whether pseudo-unbound templates are included or not. Similar improvements were also observed when combining our ensemble docking strategy with other scoring functions. The present homology model-based ensemble docking approach will have a general application in molecular docking for other interactions.

AVAILABILITY AND IMPLEMENTATION

http://huanglab.phys.hust.edu.cn/mprdock/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Molecular Docking: Challenges, Advances and its Use in Drug Discovery Perspective

Molecular docking is a process through which small molecules are docked into the macromolecular structures for scoring its complementary values at the binding sites. It is a vibrant research area with dynamic utility in structure-based drug-designing, lead optimization, biochemical pathway and for drug designing being the most attractive tools. Two pillars for a successful docking experiment are correct pose and affinity prediction. Each program has its own advantages and drawbacks with respect to their docking accuracy, ranking accuracy and time consumption so a general conclusion cannot be drawn. Moreover, users don't always consider sufficient diversity in their test sets which results in certain programs to outperform others. In this review, the prime focus has been laid on the challenges of docking and troubleshooters in existing programs, underlying algorithmic background of docking, preferences regarding the use of docking programs for best results illustrated with examples, comparison of performance for existing tools and algorithms, state of art in docking, recent trends of diseases and current drug industries, evidence from clinical trials and post-marketing surveillance are discussed. These aspects of the molecular drug designing paradigm are quite controversial and challenging and this review would be an asset to the bioinformatics and drug designing communities.

Molecular Mechanism of Evolution and Human Infection with SARS-CoV-2

The outbreak of a novel coronavirus, which was later formally named the severe acute respiratory coronavirus 2 (SARS-CoV-2), has caused a worldwide public health crisis. Previous studies showed that SARS-CoV-2 is highly homologous to SARS-CoV and infects humans through the binding of the spike protein to ACE2. Here, we have systematically studied the molecular mechanisms of human infection with SARS-CoV-2 and SARS-CoV by protein-protein docking and MD simulations. It was found that SARS-CoV-2 binds ACE2 with a higher affinity than SARS-CoV, which may partly explain that SARS-CoV-2 is much more infectious than SARS-CoV. In addition, the spike protein of SARS-CoV-2 has a significantly lower free energy than that of SARS-CoV, suggesting that SARS-CoV-2 is more stable and may survive a higher temperature than SARS-CoV. This provides insights into the evolution of SARS-CoV-2 because SARS-like coronaviruses have originated in bats. Our computation also suggested that the RBD-ACE2 binding for SARS-CoV-2 is much more temperature-sensitive than that for SARS-CoV. Thus, it is expected that SARS-CoV-2 would decrease its infection ability much faster than SARS-CoV when the temperature rises. These findings would be beneficial for the disease prevention and drug/vaccine development of SARS-CoV-2.

Doublecortin undergo nucleocytoplasmic transport via the RanGTPase signaling to promote glioma progression

Background

Nuclear translocation of several oncogenic proteins have previously been reported, but neither the translocation of doublecortin (DCX) nor the mechanism involved has been studied. DCX is a neuronal microtubule-associated protein (MAP) that is crucial for adult neurogenesis and neuronal migration and has been associated with poor prognosis in gliomas.

Methods

We probed DCX expression in different grades of glioma tissues and conventional cells via western blotting. Then we analyzed the expression pattern in the Oncomine cancer profiling database. Confocal Immunofluorescence was used to detect DCX expression in the cellular compartments, while subcellular fractionation was probed via western blotting. Pulse shape height analysis was utilized to verify DCX localization in a larger population of cells. Co-immunoprecipitation was used in detecting DCX-import receptors interactions. To probe for DCX functions, stable cells expressing high DCX expression or knockdown were generated using CRISPR-Cas9 viral transfection, while plasmid site-directed mutant constructs were used to validate putative nuclear localization sequence (NLS) predicted via conventional algorithms and comparison with classical NLSs. in-silico modeling was performed to validate DCX interactions with import receptors via the selected putative NLS. Effects of DCX high expression, knockdown, mutation, and/or deletion of putative NLS sites were probed via Boyden’s invasion assay and wound healing migration assays, and viability was detected by CCK8 assays in-vitro, while xenograft tumor model was performed in nude mice.

Results

DCX undergoes nucleocytoplasmic movement via the RanGTPase signaling pathway with an NLS located on the N-terminus between serine47-tyrosine70. This translocation could be stimulated by MARK’s phosphorylation of the serine 47 residue flanking the NLS due to aberrant expression of glial cell line-derived neurotrophic factor (GDNF). High expression and nuclear accumulation of DCX improve invasive glioma abilities in-vitro and in-vivo. Moreover, knocking down or blocking DCX nuclear import attenuates invasiveness and proliferation of glioma cells.

Conclusion

Collectively, this study highlights a remarkable phenomenon in glioma, hence revealing potential glioma dependencies on DCX expression, which is amenable to targeted therapy.

Structure-Based Virtual Screening of Pseudomonas aeruginosa LpxA Inhibitors Using Pharmacophore-Based Approach

Multidrug resistance in Pseudomonas aeruginosa is a noticeable and ongoing major obstacle for inhibitor design. In P. aeruginosa, uridine diphosphate N-acetylglucosamine (UDP-GlcNAc) acetyltransferase (PaLpxA) is an essential enzyme of lipid A biosynthesis and an attractive drug target. PaLpxA is a homotrimer, and the binding pocket for its substrate, UDP-GlcNAc, is positioned between the monomer A-monomer B interface. The uracil moiety binds at one monomer A, the GlcNAc moiety binds at another monomer B, and a diphosphate form bonds with both monomers. The catalytic residues are conserved and display a similar catalytic mechanism across orthologs, but some distinctions exist between pocket sizes, residue differences, substrate positioning and specificity. The analysis of diversified pockets, volumes, and ligand positions was determined between orthologues that could aid in selective inhibitor development. Thenceforth, a complex-based pharmacophore model was generated and subjected to virtual screening to identify compounds with similar pharmacophoric properties. Docking and general Born-volume integral (GBVI) studies demonstrated 10 best lead compounds with selective inhibition properties with essential residues in the pocket. For biological access, these scaffolds complied with the Lipinski rule, no toxicity and drug likeness properties, and were considered as lead compounds. Hence, these scaffolds could be helpful for the development of potential selective PaLpxA inhibitors.

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.

Modeling Protein-Protein or Protein-DNA/RNA Complexes Using the HDOCK Webserver

Protein-protein and protein-DNA/RNA interactions are involved in many cellular processes. Therefore, determining their complex structures at the atomic level is valuable to gain insights into these interactions. Because of the technical difficulties and high cost in experimental methods, computational approaches like molecular docking have been developed to predict the structures of macromolecular complexes in the last decades. To automatically integrate the available binding information from the PDB, we have developed HDOCK, a protein-protein/nucleic acid docking web server by combining template-based and free docking. In this chapter, we first briefly introduce our HDOCK server and then give a step-by-step description of docking bovine chymotrypsinogen A against its inhibitor (PDB ID: 1CGI). Two case studies of realistic examples are also discussed. The HDOCK server is freely available at http://hdock.phys.hust.edu.cn/ .

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/.

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

Summary

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

Availability and implementation

PepBDB is available at http://huanglab.phys.hust.edu.cn/pepbdb/.

Exploring the Binding Mechanism and Dynamics of EndoMS/NucS to Mismatched dsDNA

The well-known mismatch repair (MMR) machinery, MutS/MutL, is absent in numerous Archaea and some Bacteria. Recent studies have shown that EndoMS/NucS has the ability to cleave double-stranded DNA (dsDNA) containing a mismatched base pair, which suggests a novel mismatch repair process. However, the recognition mechanism and the binding process of EndoMS/NucS in the MMR pathway remain unclear. In this study, we investigate the binding dynamics of EndoMS/NucS to mismatched dsDNA and its energy as a function of the angle between the two C-terminal domains of EndoMS/NucS, through molecular docking and extensive molecular dynamics (MD) simulations. It is found that there exists a half-open transition state corresponding to an energy barrier (at an activation angle of approximately 80∘) between the open state and the closed state, according to the energy curve. When the angle is larger than the activation angle, the C-terminal domains can move freely and tend to change to the open state (local energy minimum). Otherwise, the C-terminal domains will interact with the mismatched dsDNA directly and converge to the closed state at the global energy minimum. As such, this two-state system enables the exposed N-terminal domains of EndoMS/NucS to recognize mismatched dsDNA during the open state and then stabilize the binding of the C-terminal domains of EndoMS/NucS to the mismatched dsDNA during the closed state. We also investigate how the EndoMS/NucS recognizes and binds to mismatched dsDNA, as well as the effects of K+ ions. The results provide insights into the recognition and binding mechanisms of EndoMS/NucS to mismatched dsDNA in the MMR pathway.

Prediction of Structure and Molecular Interaction with DNA of BvrR, a Virulence-Associated Regulatory Protein of Brucella

Brucellosis, also known as "undulant fever" is a zoonotic disease caused by Brucella, which is a facultative intracellular bacterium. Despite efforts to eradicate this disease, infection in uncontrolled domestic animals persists in several countries and therefore transmission to humans is common. Brucella evasion of the innate immune system depends on its ability to evade the mechanisms of intracellular death in phagocytic cells. The BvrR-BvrS two-component system allows the bacterium to detect adverse conditions in the environment. The BvrS protein has been associated with genes of virulence factors, metabolism, and membrane transport. In this study, we predicted the DNA sequence recognized by BvrR with Gibbs Recursive Sampling and identified the three-dimensional structure of BvrR using I-TASSER suite, and the interaction mechanism between BvrR and DNA with Protein-DNA docking and molecular dynamics (MD) simulation. Based on the Gibbs recursive Sampling analysis, we found the motif AAHTGC (H represents A, C, and T nucleotides) as a possible sequence recognized by BvrR. The docking and EMD simulation results showed that C-terminal effector domain of BvrR protein is likely to interact with AAHTGC sequence. In conclusion, we predicted the structure, recognition motif, and interaction of BvrR with DNA.

HPEPDOCK: a web server for blind peptide-protein docking based on a hierarchical algorithm

Protein-peptide interactions are crucial in many cellular functions. Therefore, determining the structure of protein-peptide complexes is important for understanding the molecular mechanism of related biological processes and developing peptide drugs. HPEPDOCK is a novel web server for blind protein-peptide docking through a hierarchical algorithm. Instead of running lengthy simulations to refine peptide conformations, HPEPDOCK considers the peptide flexibility through an ensemble of peptide conformations generated by our MODPEP program. For blind global peptide docking, HPEPDOCK obtained a success rate of 33.3% in binding mode prediction on a benchmark of 57 unbound cases when the top 10 models were considered, compared to 21.1% for pepATTRACT server. HPEPDOCK also performed well in docking against homology models and obtained a success rate of 29.8% within top 10 predictions. For local peptide docking, HPEPDOCK achieved a high success rate of 72.6% on a benchmark of 62 unbound cases within top 10 predictions, compared to 45.2% for HADDOCK peptide protocol. Our HPEPDOCK server is computationally efficient and consumed an average of 29.8 mins for a global peptide docking job and 14.2 mins for a local peptide docking job. The HPEPDOCK web server is available at http://huanglab.phys.hust.edu.cn/hpepdock/.

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.

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/.

Toward computer-made artificial antibiotics

Merging concepts from synthetic biology and computational biology may yield antibiotics that are less likely to elicit resistance than existing drugs and that yet can fight drug-resistant infections. Indeed, computer-guided strategies coupled with massively parallel high-throughput experimental methods represent a new paradigm for antibiotic discovery. Infections caused by multidrug-resistant microorganisms are increasingly deadly. In the current post-antibiotic era, many of these infections cannot be treated with our existing antimicrobial arsenal. Furthermore, we may have already exhausted the category of large molecules produced in nature having antimicrobial activity: the antibiotic scaffolds we have discovered so far may represent the majority of those that exist. The rise in drug-resistant bacteria and lack of new antibiotic classes clearly call for out-of-the-box strategies. Recent advances in computational synthetic biology have enabled the development of antimicrobials. New molecular descriptors and genetic and pattern recognition algorithms are powerful tools that bring us a step closer to developing efficient antibiotics. We review several computational tools for drug design and a number of recently generated antibiotic candidates, with an emphasis on peptide-based molecules. Design strategies can generate a diversity of synthetic antimicrobial peptides, which may help to mitigate the spread of resistance and combat multidrug-resistant microorganisms.

Bioinformatics analysis of evolutionary characteristics and biochemical structure of FGF5 Gene in sheep

Fibroblast growth factor (FGF) 5 regulates the development and periodicity of hair follicles, which can affect hair traits. Loss-of-function mutations associated with long-hair phenotypes have been described in several mammalian species. Sheep is an important economic animal, however, the evolution characterizations and biological mechanism of oFGF5 (Ovis aries FGF5) gene are still poorly understood. In this study, oFGF5 gene was obtained by resequencing the whole genome of three Dorper sheep and RACE of two Kazakh sheep FGF5. We proposed FGF5 was phylogenetically related to FGF4 family and oFGF5 clearly orthologed to goat FGF5. Six loci were found from the positive selection results of FGF5 and half of them located on signal peptide. The basically similar rates of function-altering substitutions in sheep and goat lineage and the rest of the mammalian lineage of 365 SNPs indicated that the FGF5 gene was quite conservative during evolution. Homology modeling of the oFGF5 suggested that it has a highly conserved FGF superfamily domain containing 10 β-strands. Furthermore, the protein-protein docking analysis revealed that oFGF5 have the potential to form heterodimers with oFGFR1, the predicted interaction interface of FGF5-FGFR1 heterodimer was formed mainly by residues from FGF superfamily domain. Our observations suggested the evolutionary and structural biology features of oFGF5 might be relevant to its function about hair follicle development and modulating hair growth, and we confirmed our speculation by using the FGF5 gene editing sheep produced by CRISPR/Cas9 technology.

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/.

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.

Efficient conformational ensemble generation of protein-bound peptides

Conformation generation of protein-bound peptides is critical for the determination of protein–peptide complex structures. Despite significant progress in conformer generation of small molecules, few methods have been developed for modeling protein-bound peptide conformations. Here, we have developed a fast de novo peptide modeling algorithm, referred to as MODPEP, for conformational sampling of protein-bound peptides. Given a sequence, MODPEP builds the peptide 3D structure from scratch by assembling amino acids or helix fragments based on constructed rotamer and helix libraries. The MODPEP algorithm was tested on a diverse set of 910 experimentally determined protein-bound peptides with 3–30 amino acids from the PDB and obtained an average accuracy of 1.90 Å when 200 conformations were sampled for each peptide. On average, MODPEP obtained a success rate of 74.3% for all the 910 peptides and ≥ 90% for short peptides with 3–10 amino acids in reproducing experimental protein-bound structures. Comparative evaluations of MODPEP with three other conformer generation methods, PEP-FOLD3, RDKit, and Balloon, have also been performed in both accuracy and success rate. MODPEP is fast and can generate 100 conformations for less than one second. The fast MODPEP will be beneficial for large-scale de novo modeling and docking of peptides. The MODPEP program and libraries are available for download at http://huanglab.phys.hust.edu.cn/.