Template-based and free modeling of I-TASSER and QUARK pipelines using predicted contact maps in CASP12

Computer-aided re-engineering of nonribosomal peptide and polyketide biosynthetic assembly lines

Covering: 2014 to 2019Nonribosomal peptide synthetases (NRPSs) and polyketide synthases (PKSs) have been the subject of engineering efforts for multiple decades. Their modular assembly line architecture potentially allows unlocking vast chemical space for biosynthesis. However, attempts thus far are often met with mixed success, due to limited molecular compatibility of the parts used for engineering. Now, new engineering strategies, increases in genomic data, and improved computational tools provide more opportunities for major progress. In this review we highlight some of the challenges and progressive strategies for the re-design of NRPSs & type I PKSs and survey useful computational tools and approaches to attain the ultimate goal of semi-automated and design-based engineering of novel peptide and polyketide products.

Predicting Protein Dimer Structures Using MELD × MD

It is challenging to predict the docked conformations of two proteins. Current methods are susceptible to errors from treating proteins as rigid bodies and from an inability to compute relative Boltzmann populations of different docked conformations. Here, we show that by using the ClusPro server as a front end to generate possible protein-protein contacts, and using Modeling Employing Limited Data (MELD) accelerated molecular dynamics (MELD × MD) as a back end for atomistic simulations, we can find 16/20 native dimer structures of small proteins as those having the lowest free energy, starting from good-bound-backbone structures. We show that atomistic MD free energies can be used to identify native protein dimer structures.

Mixed lineage kinase 3 promotes breast tumorigenesis via phosphorylation and activation of p21-activated kinase 1

Mixed lineage kinase 3 (MLK3), a MAP3K member has been envisioned as a viable drug target in cancer, yet its detailed function and signaling is not fully elucidated. We identified that MLK3 tightly associates with an oncogene, PAK1. Mammalian PAK1 being a Ste20 (MAP4K) member, we tested whether it is an upstream regulator of MLK3. In contrast to our hypothesis, MLK3 activated PAK1 kinase activity directly, as well as in the cells. Although, MLK3 can phosphorylate PAK1 on Ser133 and Ser204 sites, PAK1S133A mutant is constitutively active, whereas, PAK1S204A is not activated by MLK3. Stable overexpression of PAK1S204A in breast cancer cells, impedes migration, invasion, and NFĸB activity. In vivo breast cancer cell tumorigenesis is significantly reduced in tumors expressing PAK1S204A mutant. These results suggest that mammalian PAK1 does not act as a MAP4K and MLK3-induced direct activation of PAK1 plays a key role in breast cancer tumorigenesis.

End-to-End Differentiable Learning of Protein Structure

Predicting protein structure from sequence is a central challenge of biochemistry. Co-evolution methods show promise, but an explicit sequence-to-structure map remains elusive. Advances in deep learning that replace complex, human-designed pipelines with differentiable models optimized end to end suggest the potential benefits of similarly reformulating structure prediction. Here, we introduce an end-to-end differentiable model for protein structure learning. The model couples local and global protein structure via geometric units that optimize global geometry without violating local covalent chemistry. We test our model using two challenging tasks: predicting novel folds without co-evolutionary data and predicting known folds without structural templates. In the first task, the model achieves state-of-the-art accuracy, and in the second, it comes within 1-2 Å; competing methods using co-evolution and experimental templates have been refined over many years, and it is likely that the differentiable approach has substantial room for further improvement, with applications ranging from drug discovery to protein design.

Phycobilisomes Harbor FNRL in Cyanobacteria

Cyanobacterial phycobilisomes (PBSs) are photosynthetic antenna complexes that harvest light energy and supply it to two reaction centers (RCs) where photochemistry starts. PBSs can be classified into two types, depending on the presence of allophycocyanin (APC): CpcG-PBS and CpcL-PBS. Because the accurate protein composition of CpcL-PBS remains unclear, we describe here its isolation and characterization from the cyanobacterium Synechocystis sp. strain 6803. We found that ferredoxin-NADP+ oxidoreductase (or FNRL), an enzyme involved in both cyclic electron transport and the terminal step of the electron transport chain in oxygenic photosynthesis, is tightly associated with CpcL-PBS as well as with CpcG-PBS. Room temperature and low-temperature fluorescence analyses show a red-shifted emission at 669 nm in CpcL-PBS as a terminal energy emitter without APC. SDS-PAGE and quantitative mass spectrometry reveal an increased content of FNRL and CpcC2, a rod linker protein, in CpcL-PBS compared to that of CpcG-PBS rods, indicative of an elongated CpcL-PBS rod length and its potential functional differences from CpcG-PBS. Furthermore, we combined isotope-encoded cross-linking mass spectrometry with computational protein structure predictions and structural modeling to produce an FNRL-PBS binding model that is supported by two cross-links between K69 of FNRL and the N terminus of CpcB, one component in PBS, in both CpcG-PBS and CpcL-PBS (cross-link 1), and between the N termini of FNRL and CpcB (cross-link 2). Our data provide a novel functional assembly form of phycobiliproteins and a molecular-level description of the close association of FNRL with phycocyanin in both CpcG-PBS and CpcL-PBS.IMPORTANCE Cyanobacterial light-harvesting complex PBSs are essential for photochemistry in light reactions and for balancing energy flow to carbon fixation in the form of ATP and NADPH. We isolated a new type of PBS without an allophycocyanin core (i.e., CpcL-PBS). CpcL-PBS contains both a spectral red-shifted chromophore, enabling efficient energy transfer to chlorophyll molecules in the reaction centers, and an increased FNRL content with various rod lengths. Identification of a close association of FNRL with both CpcG-PBS and CpcL-PBS brings new insight to its regulatory role for fine-tuning light energy transfer and carbon fixation through both noncyclic and cyclic electron transport.

In silico analysis of the V66M variant of human BDNF in psychiatric disorders: An approach to precision medicine

Brain-derived neurotrophic factor (BDNF) plays an important role in neurogenesis and synapse formation. The V66M is the most prevalent BDNF mutation in humans and impairs the function and distribution of BDNF. This mutation is related to several psychiatric disorders. The pro-region of BDNF, particularly position 66 and its adjacent residues, are determinant for the intracellular sorting and activity-dependent secretion of BDNF. However, it has not yet been fully elucidated. The present study aims to analyze the effects of the V66M mutation on BDNF structure and function. Here, we applied nine algorithms, including SIFT and PolyPhen-2, for functional and stability prediction of the V66M mutation. The complete theoretical model of BNDF was generated by Rosetta and validated by PROCHECK, RAMPAGE, ProSa, QMEAN and Verify-3D algorithms. Structural alignment was performed using TM-align. Phylogenetic analysis was performed using the ConSurf server. Molecular dynamics (MD) simulations were performed and analyzed using the GROMACS 2018.2 package. The V66M mutation was predicted as deleterious by PolyPhen-2 and SIFT in addition to being predicted as destabilizing by I-Mutant. According to SNPeffect, the V66M mutation does not affect protein aggregation, amyloid propensity, and chaperone binding. The complete theoretical structure of BDNF proved to be a reliable model. Phylogenetic analysis indicated that the V66M mutation of BDNF occurs at a non-conserved position of the protein. MD analyses indicated that the V66M mutation does not affect the BDNF flexibility and surface-to-volume ratio, but affects the BDNF essential motions, hydrogen-bonding and secondary structure particularly at its pre and pro-domain, which are crucial for its activity and distribution. Thus, considering that these parameters are determinant for protein interactions and, consequently, protein function; the alterations observed throughout the MD analyses may be related to the functional impairment of BDNF upon V66M mutation, as well as its involvement in psychiatric disorders.

Can Predicted Protein 3D Structures Provide Reliable Insights into whether Missense Variants Are Disease Associated?

Knowledge of protein structure can be used to predict the phenotypic consequence of a missense variant. Since structural coverage of the human proteome can be roughly tripled to over 50% of the residues if homology-predicted structures are included in addition to experimentally determined coordinates, it is important to assess the reliability of using predicted models when analyzing missense variants. Accordingly, we assess whether a missense variant is structurally damaging by using experimental and predicted structures. We considered 606 experimental structures and show that 40% of the 1965 disease-associated missense variants analyzed have a structurally damaging change in the mutant structure. Only 11% of the 2134 neutral variants are structurally damaging. Importantly, similar results are obtained when 1052 structures predicted using Phyre2 algorithm were used, even when the model shares low (<40%) sequence identity to the template. Thus, structure-based analysis of the effects of missense variants can be effectively applied to homology models. Our in-house pipeline, Missense3D, for structurally assessing missense variants was made available at http://www.sbg.bio.ic.ac.uk/~missense3d.

Electrostatics of Tau Protein by Molecular Dynamics

Tau is a microtubule-associated protein that promotes microtubule assembly and stability. This protein is implicated in several neurodegenerative diseases, including Alzheimer’s. To date, the three-dimensional (3D) structure of tau has not been fully solved, experimentally. Even the most recent information is sometimes controversial in regard to how this protein folds, interacts, and behaves. Predicting the tau structure and its profile sheds light on the knowledge about its properties and biological function, such as the binding to microtubules (MT) and, for instance, the effect on ionic conductivity. Our findings on the tau structure suggest a disordered protein, with discrete portions of well-defined secondary structure, mostly at the microtubule binding region. In addition, the first molecular dynamics simulation of full-length tau along with an MT section was performed, unveiling tau structure when associated with MT and interaction sites. Electrostatics and conductivity were also examined to understand how tau affects the ions in the intracellular fluid environment. Our results bring a new insight into tau and tubulin MT proteins, their characteristics, and the structure–function relationship.

Cryo-EM Structure and Assembly of an Extracellular Contractile Injection System

Contractile injection systems (CISs) are cell-puncturing nanodevices that share ancestry with contractile tail bacteriophages. Photorhabdus virulence cassette (PVC) represents one group of extracellular CISs that are present in both bacteria and archaea. Here, we report the cryo-EM structure of an intact PVC from P. asymbiotica. This over 10-MDa device resembles a simplified T4 phage tail, containing a hexagonal baseplate complex with six fibers and a capped 117-nanometer sheath-tube trunk. One distinct feature of the PVC is the presence of three variants for both tube and sheath proteins, indicating a functional specialization of them during evolution. The terminal hexameric cap docks onto the topmost layer of the inner tube and locks the outer sheath in pre-contraction state with six stretching arms. Our results on the PVC provide a framework for understanding the general mechanism of widespread CISs and pave the way for using them as delivery tools in biological or therapeutic applications.

DAMpred: Recognizing Disease-Associated nsSNPs through Bayes-Guided Neural-Network Model Built on Low-Resolution Structure Prediction of Proteins and Protein-Protein Interactions

Nearly one-third of non-synonymous single-nucleotide polymorphism (nsSNPs) are deleterious to human health, but recognition of the disease-associated mutations remains a significant unsolved problem. We proposed a new algorithm, DAMpred, to identify disease-causing nsSNPs through the coupling of evolutionary profiles with structure predictions of proteins and protein-protein interactions. The pipeline was trained by a novel Bayes-guided artificial neural network algorithm that incorporates posterior probabilities of distinct feature classifiers with the network training process. DAMpred was tested on a large-scale data set involving 10,635 nsSNPs from 2154 ORFs in the human genome and recognized disease-associated nsSNPs with an accuracy 0.80 and a Matthews correlation coefficient of 0.601, which is 9.1% higher than the best of other state-of-the-art methods. In the blind test on the TP53 gene, DAMpred correctly recognized the mutations causative of Li-Fraumeni-like syndrome with a Matthews correlation coefficient that is 27% higher than the control methods. The study demonstrates an efficient avenue to quantitatively model the association of nsSNPs with human diseases from low-resolution protein structure prediction, which should find important usefulness in diagnosis and treatment of genetic diseases.

Structure and Protein Interaction-Based Gene Ontology Annotations Reveal Likely Functions of Uncharacterized Proteins on Human Chromosome 17

Understanding the function of human proteins is essential to decipher the molecular mechanisms of human diseases and phenotypes. Of the 17 470 human protein coding genes in the neXtProt 2018-01-17 database with unequivocal protein existence evidence (PE1), 1260 proteins do not have characterized functions. To reveal the function of poorly annotated human proteins, we developed a hybrid pipeline that creates protein structure prediction using I-TASSER and infers functional insights for the target protein from the functional templates recognized by COFACTOR. As a case study, the pipeline was applied to all 66 PE1 proteins with unknown or insufficiently specific function (uPE1) on human chromosome 17 as of neXtProt 2017-07-01. Benchmark testing on a control set of 100 well-characterized proteins randomly selected from the same chromosome shows high Gene Ontology (GO) term prediction accuracies of 0.69, 0.57, and 0.67 for molecular function (MF), biological process (BP), and cellular component (CC), respectively. Three pipelines of function annotations (homology detection, protein-protein interaction network inference, and structure template identification) have been exploited by COFACTOR. Detailed analyses show that structure template detection based on low-resolution protein structure prediction made the major contribution to the enhancement of the sensitivity and precision of the annotation predictions, especially for cases that do not have sequence-level homologous templates. For the chromosome 17 uPE1 proteins, the I-TASSER/COFACTOR pipeline confidently assigned MF, BP, and CC for 13, 33, and 49 proteins, respectively, with predicted functions ranging from sphingosine N-acyltransferase activity and sugar transmembrane transporter to cytoskeleton constitution. We highlight the 13 proteins with confident MF predictions; 11 of these are among the 33 proteins with confident BP predictions and 12 are among the 49 proteins with confident CC. This study demonstrates a novel computational approach to systematically annotate protein function in the human proteome and provides useful insights to guide experimental design and follow-up validation studies of these uncharacterized proteins.

A CARD9 Founder Mutation Disrupts NF-κB Signaling by Inhibiting BCL10 and MALT1 Recruitment and Signalosome Formation

Background: Inherited CARD9 deficiency constitutes a primary immunodeficiency predisposing uniquely to chronic and invasive fungal infections. Certain mutations are shown to negatively impact CARD9 protein expression and/or NF-κB activation, but the underlying biochemical mechanism remains to be fully understood.

Objectives: To investigate a possible founder origin of a known CARD9 R70W mutation in five families of Turkish origin. To explore the biochemical mechanism of immunodeficiency by R70W CARD9.

Methods: We performed haplotype analysis using microsatellite markers and SNPs. We designed a model system exploiting a gain-of-function (GOF) CARD9 L213LI mutant that triggers constitutive NF-κB activation, analogous to an oncogenic CARD11 mutant, to study NF-κB signaling and signalosome formation. We performed reporter assays, immunoprecipitation and confocal imaging on HEK cells overexpressing different CARD9 variants.

Results: We identified a common haplotype, thus providing evidence for a common Turkish founder. CARD9 R70W failed to activate NF-κB and abrogated NF-κB activation by WT CARD9 and by GOF CARD9. Notably, R70W CARD9 also exerted negative effects on NF-κB activation by CARD10, CARD11, and CARD14. Consistent with the NF-κB results, the R70W mutation prevented GOF CARD9 to pull down the signalosome partner proteins BCL10 and MALT1. This reflected into drastic reduction of BCL10 filamentous assemblies in a cellular context. Indeed, structural analysis revealed that position R70 in CARD9 maps at the putative interface between successive CARD domains in CARD9 filaments.

Conclusions: The R70W mutation in CARD9 prevents NF-κB activation by inhibiting productive interactions with downstream BCL10 and MALT1, necessary for assembly of the filamentous CARD9-BCL10-MALT1 signalosome.

Structural Characterisation of Predicted Helical Regions in the Chironex fleckeri CfTX-1 Toxin

The Australian jellyfish Chironex fleckeri, belongs to a family of cubozoan jellyfish known for their potent venoms. CfTX-1 and -2 are two highly abundant toxins in the venom, but there is no structural data available for these proteins. Structural information on toxins is integral to the understanding of the mechanism of these toxins and the development of an effective treatment. Two regions of CfTX-1 have been predicted to have helical structures that are involved with the mechanism of action. Here we have synthesized peptides corresponding to these regions and analyzed their structures using NMR spectroscopy. The peptide corresponding to the predicted N-terminal amphiphilic helix appears unstructured in aqueous solution. This lack of structure concurs with structural disorder predicted for this region of the protein using the Protein DisOrder prediction System PrDOS. Conversely, a peptide corresponding to a predicted transmembrane region is very hydrophobic, insoluble in aqueous solution and predicted to be structured by PrDOS. In the presence of SDS-micelles both peptides have well-defined helical structures showing that a membrane mimicking environment stabilizes the structures of both peptides and supports the prediction of the transmembrane region in CfTX-1. This is the first study to experimentally analyze the structure of regions of a C. fleckeri protein.

Structural Analysis of Recombinant Human Preproinsulins by Structure Prediction, Molecular Dynamics, and Protein-Protein Docking

More effective production of human insulin is important, because insulin is the main medication that is used to treat multiple types of diabetes and because many people are suffering from diabetes. The current system of insulin production is based on recombinant DNA technology, and the expression vector is composed of a preproinsulin sequence that is a fused form of an artificial leader peptide and the native proinsulin. It has been reported that the sequence of the leader peptide affects the production of insulin. To analyze how the leader peptide affects the maturation of insulin structurally, we adapted several in silico simulations using 13 artificial proinsulin sequences. Three-dimensional structures of models were predicted and compared. Although their sequences had few differences, the predicted structures were somewhat different. The structures were refined by molecular dynamics simulation, and the energy of each model was estimated. Then, protein-protein docking between the models and trypsin was carried out to compare how efficiently the protease could access the cleavage sites of the proinsulin models. The results showed some concordance with experimental results that have been reported; so, we expect our analysis will be used to predict the optimized sequence of artificial proinsulin for more effective production.