JPred: a consensus secondary structure prediction server

A comprehensive overview of recent advances in generative models for antibodies

Therapeutic antibodies are an important class of biopharmaceuticals. With the rapid development of deep learning methods and the increasing amount of antibody data, antibody generative models have made great progress recently. They aim to solve the antibody space searching problems and are widely incorporated into the antibody development process. Therefore, a comprehensive introduction to the development methods in this field is imperative. Here, we collected 34 representative antibody generative models published recently and all generative models can be divided into three categories: sequence-generating models, structure-generating models, and hybrid models, based on their principles and algorithms. We further studied their performance and contributions to antibody sequence prediction, structure optimization, and affinity enhancement. Our manuscript will provide a comprehensive overview of the status of antibody generative models and also offer guidance for selecting different approaches.

Structural analysis of the BisI family of modification dependent restriction endonucleases

The BisI family of restriction endonucleases is unique in requiring multiple methylated or hydroxymethylated cytosine residues within a short recognition sequence (GCNGC), and in cleaving directly within this sequence, rather than at a distance. Here, we report that the number of modified cytosines that are required for cleavage can be tuned by the salt concentration. We present crystal structures of two members of the BisI family, NhoI and Eco15I_Ntd (N-terminal domain of Eco15I), in the absence of DNA and in specific complexes with tetra-methylated GCNGC target DNA. The structures show that NhoI and Eco15I_Ntd sense modified cytosine bases in the context of double-stranded DNA (dsDNA) without base flipping. In the co-crystal structures of NhoI and Eco15I_Ntd with DNA, the internal methyl groups (G5mCNGC) interact with the side chains of an (H/R)(V/I/T/M) di-amino acid motif near the C-terminus of the distal enzyme subunit and arginine residue from the proximal subunit. The external methyl groups (GCNG5mC) interact with the proximal enzyme subunit, mostly through main chain contacts. Surface plasmon resonance analysis for Eco15I_Ntd shows that the internal and external methyl binding pockets contribute about equally to sensing of cytosine methyl groups.

A unified analysis of evolutionary and population constraint in protein domains highlights structural features and pathogenic sites

Protein evolution is constrained by structure and function, creating patterns in residue conservation that are routinely exploited to predict structure and other features. Similar constraints should affect variation across individuals, but it is only with the growth of human population sequencing that this has been tested at scale. Now, human population constraint has established applications in pathogenicity prediction, but it has not yet been explored for structural inference. Here, we map 2.4 million population variants to 5885 protein families and quantify residue-level constraint with a new Missense Enrichment Score (MES). Analysis of 61,214 structures from the PDB spanning 3661 families shows that missense depleted sites are enriched in buried residues or those involved in small-molecule or protein binding. MES is complementary to evolutionary conservation and a combined analysis allows a new classification of residues according to a conservation plane. This approach finds functional residues that are evolutionarily diverse, which can be related to specificity, as well as family-wide conserved sites that are critical for folding or function. We also find a possible contrast between lethal and non-lethal pathogenic sites, and a surprising clinical variant hot spot at a subset of missense enriched positions.

Temperature-dependent action of pepper mildew resistance locus O 1 in inducing pathogen immunity and thermotolerance

Plant diseases tend to be more serious under conditions of high-temperature/high-humidity (HTHH) than under moderate conditions, and hence disease resistance under HTHH is an important determinant for plant survival. However, how plants cope with diseases under HTHH remains poorly understood. In this study, we used the pathosystem consisting of pepper (Capsicum annuum) and Ralstonia solanacearum (bacterial wilt) as a model to examine the functions of the protein mildew resistance locus O 1 (CaMLO1) and U-box domain-containing protein 21 (CaPUB21) under conditions of 80% humidity and either 28 °C or 37 °C. Expression profiling, loss- and gain-of-function assays involving virus-induced gene-silencing and overexpression in pepper plants, and protein-protein interaction assays were conducted, and the results showed that CaMLO1 acted negatively in pepper immunity against R. solanacearum at 28 °C but positively at 37 °C. In contrast, CaPUB21 acted positively in immunity at 28 °C but negatively at 37 °C. Importantly, CaPUB21 interacted with CaMLO1 under all of the tested conditions, but only the interaction in response to R. solanacearum at 37 °C or to exposure to 37 °C alone led to CaMLO1 degradation, thereby turning off defence responses against R. solanacearum at 37 °C and under high-temperature stress to conserve resources. Thus, we show that CaMLO1 and CaPUB21 interact with each other and function distinctly in pepper immunity against R. solanacearum in an environment-dependent manner.

Staphylococcus aureus functional amyloids catalyze degradation of β-lactam antibiotics

Antibiotic resistance of bacteria is considered one of the most alarming developments in modern medicine. While varied pathways for bacteria acquiring antibiotic resistance have been identified, there still are open questions concerning the mechanisms underlying resistance. Here, we show that alpha phenol-soluble modulins (PSMαs), functional bacterial amyloids secreted by Staphylococcus aureus, catalyze hydrolysis of β-lactams, a prominent class of antibiotic compounds. Specifically, we show that PSMα2 and, particularly, PSMα3 catalyze hydrolysis of the amide-like bond of the four membered β-lactam ring of nitrocefin, an antibiotic β-lactam surrogate. Examination of the catalytic activities of several PSMα3 variants allowed mapping of the active sites on the amyloid fibrils' surface, specifically underscoring the key roles of the cross-α fibril organization, and the combined electrostatic and nucleophilic functions of the lysine arrays. Molecular dynamics simulations further illuminate the structural features of β-lactam association upon the fibril surface. Complementary experimental data underscore the generality of the functional amyloid-mediated catalytic phenomenon, demonstrating hydrolysis of clinically employed β-lactams by PSMα3 fibrils, and illustrating antibiotic degradation in actual S. aureus biofilms and live bacteria environments. Overall, this study unveils functional amyloids as catalytic agents inducing degradation of β-lactam antibiotics, underlying possible antibiotic resistance mechanisms associated with bacterial biofilms.

Some Structural Elements of Bacterial Protein MF3 That Influence Its Ability to Induce Plant Resistance to Fungi, Viruses, and Other Plant Pathogens

The ability of the MF3 protein from Pseudomonas fluorescens to protect plants by inducing their resistance to pathogenic fungi, bacteria, and viruses is well confirmed both in greenhouses and in the field; however, the molecular basis of this phenomenon remains unexplored. To find a relationship between the primary (and spatial) structure of the protein and its target activity, we analyzed the inducing activity of a set of mutants generated by alanine scanning and an alpha-helix deletion (ahD) in the part of the MF3 molecule previously identified by our group as a 29-amino-acid peptide working as the inducer on its own. Testing the mutants' inducing activity using the "tobacco-tobacco mosaic virus" pathosystem revealed that some of them showed an almost threefold (V60A and V62A) or twofold (G51A, L58A, ahD) reduction in inducing activity compared to the wild-type MF3 type. Interestingly, these mutations demonstrated close proximity in the homology model, probably contributing to MF3 reception in a host plant.

Reverse Vaccinology and Immunoinformatic Approach for Designing a Bivalent Vaccine Candidate Against Hepatitis A and Hepatitis B Viruses

Hepatitis A and B are two crucial viral infections that still dramatically affect public health worldwide. Hepatitis A Virus (HAV) is the main cause of acute hepatitis, whereas Hepatitis B Virus (HBV) leads to the chronic form of the disease, possibly cirrhosis or liver failure. Therefore, vaccination has always been considered the most effective preventive method against pathogens. At this moment, we aimed at the immunoinformatic analysis of HAV-Viral Protein 1 (VP1) as the major capsid protein to come up with the most conserved immunogenic truncated protein to be fused by HBV surface antigen (HBs Ag) to achieve a bivalent vaccine against HAV and HBV using an AAY linker. Various computational approaches were employed to predict highly conserved regions and the most immunogenic B-cell and T-cell epitopes of HAV-VP1 capsid protein in both humans and BALB/c. Moreover, the predicted fusion protein was analyzed regarding primary and secondary structures and also homology validation. Afterward, the three-dimensional structure of vaccine constructs docked with various toll-like receptors (TLR) 2, 4 and 7. According to the bioinformatics tools, the region of 99-259 amino acids of VP1 was selected with high immunogenicity and conserved epitopes. T-cell epitope prediction showed that this region contains 32 antigenic peptides for Human leukocyte antigen (HLA) class I and 20 antigenic peptides in terms of HLA class II which are almost fully conserved in the Iranian population. The vaccine design includes 5 linear and 4 conformational B-cell lymphocyte (BCL) epitopes to induce humoral immune responses. The designed VP1-AAY-HBsAg fusion protein has the potency to be constructed and expressed to achieve a bivalent vaccine candidate, especially in the Iranian population. These findings led us to claim that the designed vaccine candidate provides potential pathways for creating an exploratory vaccine against Hepatitis A and Hepatitis B Viruses with high confidence for the identified strains.

Structural basis of protein condensation on microtubules underlying branching microtubule nucleation

Targeting protein for Xklp2 (TPX2) is a key factor that stimulates branching microtubule nucleation during cell division. Upon binding to microtubules (MTs), TPX2 forms condensates via liquid-liquid phase separation, which facilitates recruitment of microtubule nucleation factors and tubulin. We report the structure of the TPX2 C-terminal minimal active domain (TPX2α5-α7) on the microtubule lattice determined by magic-angle-spinning NMR. We demonstrate that TPX2α5-α7 forms a co-condensate with soluble tubulin on microtubules and binds to MTs between two adjacent protofilaments and at the intersection of four tubulin heterodimers. These interactions stabilize the microtubules and promote the recruitment of tubulin. Our results reveal that TPX2α5-α7 is disordered in solution and adopts a folded structure on MTs, indicating that TPX2α5-α7 undergoes structural changes from unfolded to folded states upon binding to microtubules. The aromatic residues form dense interactions in the core, which stabilize folding of TPX2α5-α7 on microtubules. This work informs on how the phase-separated TPX2α5-α7 behaves on microtubules and represents an atomic-level structural characterization of a protein that is involved in a condensate on cytoskeletal filaments.

Function and structure of bradykinin receptor 2 for drug discovery

Type 2 bradykinin receptor (B2R) is an essential G protein-coupled receptor (GPCR) that regulates the cardiovascular system as a vasodepressor. Dysfunction of B2R is also closely related to cancers and hereditary angioedema (HAE). Although several B2R agonists and antagonists have been developed, icatibant is the only B2R antagonist clinically used for treating HAE. The recently determined structures of B2R have provided molecular insights into the functions and regulation of B2R, which shed light on structure-based drug design for the treatment of B2R-related diseases. In this review, we summarize the structure and function of B2R in relation to drug discovery and discuss future research directions to elucidate the remaining unknown functions of B2R dimerization.

Structural analysis of human G-protein-coupled receptor 17 ligand binding sites

The human G protein coupled membrane receptor (GPR17), the sensor of brain damage, is identified as a biomarker for many neurological diseases. In human brain tissue, GPR17 exist in two isoforms, long and short. While cryo-electron microscopy technology has provided the structure of the long isoform of GPR17 with Gi complex, the structure of the short isoform and its activation mechanism remains unclear. Recently, we theoretically modeled the structure of the short isoform of GPR17 with Gi signaling protein and identified novel ligands. In the present work, we demonstrated the presence of two distinct ligand binding sites in the short isoform of GPR17. The molecular docking of GPR17 with endogenous (UDP) and synthetic ligands (T0510.3657, MDL29950) found the presence of two distinct binding pockets. Our observations revealed that endogenous ligand UDP can bind stronger in two different binding pockets as evidenced by glide and autodock vina scores, whereas the other two ligand's binding with GPR17 has less docking score. The analysis of receptor-UDP interactions shows complexes' stability in the lipid environment by 100 ns atomic molecular dynamics simulations. The amino acid residues VAL83, ARG87, and PHE111 constitute ligand binding site 1, whereas site 2 constitutes ASN67, ARG129, and LYS232. Root mean square fluctuation analysis showed the residues 83, 87, and 232 with higher fluctuations during molecular dynamics simulation in both binding pockets. Our findings imply that the residues of GPR17's two binding sites are crucial, and their interaction with UDP reveals the protein's hidden signaling and communication properties. Furthermore, this finding may assist in the development of targeted therapies for the treatment of neurological diseases.

Illuminating the "Twilight Zone": Advances in Difficult Protein Modeling

Homology modeling was long considered a method of choice in tertiary protein structure prediction. However, it used to provide models of acceptable quality only when templates with appreciable sequence identity with a target could be found. The threshold value was long assumed to be around 20-30%. Below this level, obtained sequence identity was getting dangerously close to values that can be obtained by chance, after aligning any random, unrelated sequences. In these cases, other approaches, including ab initio folding simulations or fragment assembly, were usually employed. The most recent editions of the CASP and CAMEO community-wide modeling methods assessment have brought some surprising outcomes, proving that much more clues can be inferred from protein sequence analyses than previously thought. In this chapter, we focus on recent advances in the field of difficult protein modeling, pushing the threshold deep into the "twilight zone", with particular attention devoted to improvements in applications of machine learning and model evaluation.

Dimerisation of the Yeast K+ Translocation Protein Trk1 Depends on the K+ Concentration

In baker's yeast (Saccharomyces cerevisiae), Trk1, a member of the superfamily of K-transporters (SKT), is the main K⁺ uptake system under conditions when its concentration in the environment is low. Structurally, Trk1 is made up of four domains, each similar and homologous to a K-channel α subunit. Because most K-channels are proteins containing four channel-building α subunits, Trk1 could be functional as a monomer. However, related SKT proteins TrkH and KtrB were crystallised as dimers, and for Trk1, a tetrameric arrangement has been proposed based on molecular modelling. Here, based on Bimolecular Fluorescence Complementation experiments and single-molecule fluorescence microscopy combined with molecular modelling; we provide evidence that Trk1 can exist in the yeast plasma membrane as a monomer as well as a dimer. The association of monomers to dimers is regulated by the K⁺ concentration.

The coilin N-terminus mediates multivalent interactions between coilin and Nopp140 to form and maintain Cajal bodies

Cajal bodies (CBs) are ubiquitous nuclear membraneless organelles (MLOs) that concentrate and promote efficient biogenesis of snRNA-protein complexes involved in splicing (snRNPs). Depletion of the CB scaffolding protein coilin disperses snRNPs, making CBs a model system for studying the structure and function of MLOs. Although it is assumed that CBs form through condensation, the biomolecular interactions responsible remain elusive. Here, we discover the unexpected capacity of coilin’s N-terminal domain (NTD) to form extensive fibrils in the cytoplasm and discrete nuclear puncta in vivo. Single amino acid mutational analysis reveals distinct molecular interactions between coilin NTD proteins to form fibrils and additional NTD interactions with the nuclear Nopp140 protein to form puncta. We provide evidence that Nopp140 has condensation capacity and is required for CB assembly. From these observations, we propose a model in which coilin NTD–NTD mediated assemblies make multivalent contacts with Nopp140 to achieve biomolecular condensation in the nucleus.

Cajal bodies are membraneless organelles scaffolded by coilin protein. Here, coilin–coilin and coilin–Nopp140 interaction sites are identified and perturbed, revealing coilin’s capacity to form long fibrils and be remodeled into spherical structures.

PYK2 senses calcium through a disordered dimerization and calmodulin-binding element

Multidomain kinases use many ways to integrate and process diverse stimuli. Here, we investigated the mechanism by which the protein tyrosine kinase 2-beta (PYK2) functions as a sensor and effector of cellular calcium influx. We show that the linker between the PYK2 kinase and FAT domains (KFL) encompasses an unusual calmodulin (CaM) binding element. PYK2 KFL is disordered and engages CaM through an ensemble of transient binding events. Calcium increases the association by promoting structural changes in CaM that expose auxiliary interaction opportunities. KFL also forms fuzzy dimers, and dimerization is enhanced by CaM binding. As a monomer, however, KFL associates with the PYK2 FERM-kinase fragment. Thus, we identify a mechanism whereby calcium influx can promote PYK2 self-association, and hence kinase-activating trans-autophosphorylation. Collectively, our findings describe a flexible protein module that expands the paradigms for CaM binding and self-association, and their use for controlling kinase activity.

Protein tyrosine kinase 2-beta is shown to function as a sensor and effector of cellular calcium influx through self-association.

A PadR family transcriptional repressor controls transcription of a trivalent metalloid resistance operon of Azospirillum halopraeferens strain Au 4

Methylarsenite [MAs(III)] is a highly toxic arsenical produced by some microbes as an antibiotic. In this study, we demonstrate that a PadR family transcriptional regulator, PadR_ars , from Azospirillum halopraeferens strain Au 4 directly binds to the promoter region of the arsenic resistance (ars) operon (consisting of padR_ars , arsV, and arsW) and represses transcription of arsV and arsW genes involved in MAs(III) resistance. Quantitative reverse transcriptase PCR and transcriptional reporter assays showed that transcription of the ars operon is induced strongly by MAs(III) and less strongly by arsenite and antimonite. Electrophoretic mobility shift assays with recombinant PadR_ars showed that it represses transcription of the ars operon by binding to two inverted-repeat sequences within the ars promoter. PadR_ars has two conserved cysteine pairs, Cys56/57 and Cys133/134; mutation of the first pair to serine abolished the transcriptional response of the ars operon to trivalent metalloids, suggesting that Cys56/57 form a binding site for trivalent metalloids. Either C133S or C134S derivative responses to MAs(III) but not As(III) or Sb(III), suggesting that it is a third ligand to trivalent metalloids. PadR_ars represents a new type of repressor proteins regulating transcription of an ars operon involved in the resistance to trivalent metalloids, especially MAs(III). This article is protected by copyright. All rights reserved.

Prediction of protein secondary structure based on an improved channel attention and multiscale convolution module

Prediction of the protein secondary structure is a key issue in protein science. Protein secondary structure prediction (PSSP) aims to construct a function that can map the amino acid sequence into the secondary structure so that the protein secondary structure can be obtained according to the amino acid sequence. Driven by deep learning, the prediction accuracy of the protein secondary structure has been greatly improved in recent years. To explore a new technique of PSSP, this study introduces the concept of an adversarial game into the prediction of the secondary structure, and a conditional generative adversarial network (GAN)-based prediction model is proposed. We introduce a new multiscale convolution module and an improved channel attention (ICA) module into the generator to generate the secondary structure, and then a discriminator is designed to conflict with the generator to learn the complicated features of proteins. Then, we propose a PSSP method based on the proposed multiscale convolution module and ICA module. The experimental results indicate that the conditional GAN-based protein secondary structure prediction (CGAN-PSSP) model is workable and worthy of further study because of the strong feature-learning ability of adversarial learning.

Solution structure of the type I polyketide synthase Pks13 from Mycobacterium tuberculosis

BACKGROUND

Type I polyketide synthases (PKSs) are multifunctional enzymes responsible for the biosynthesis of a group of diverse natural compounds with biotechnological and pharmaceutical interest called polyketides. The diversity of polyketides is impressive despite the limited set of catalytic domains used by PKSs for biosynthesis, leading to considerable interest in deciphering their structure-function relationships, which is challenging due to high intrinsic flexibility. Among nineteen polyketide synthases encoded by the genome of Mycobacterium tuberculosis, Pks13 is the condensase required for the final condensation step of two long acyl chains in the biosynthetic pathway of mycolic acids, essential components of the cell envelope of Corynebacterineae species. It has been validated as a promising druggable target and knowledge of its structure is essential to speed up drug discovery to fight against tuberculosis.

RESULTS

We report here a quasi-atomic model of Pks13 obtained using small-angle X-ray scattering of the entire protein and various molecular subspecies combined with known high-resolution structures of Pks13 domains or structural homologues. As a comparison, the low-resolution structures of two other mycobacterial polyketide synthases, Mas and PpsA from Mycobacterium bovis BCG, are also presented. This study highlights a monomeric and elongated state of the enzyme with the apo- and holo-forms being identical at the resolution probed. Catalytic domains are segregated into two parts, which correspond to the condensation reaction per se and to the release of the product, a pivot for the enzyme flexibility being at the interface. The two acyl carrier protein domains are found at opposite sides of the ketosynthase domain and display distinct characteristics in terms of flexibility.

CONCLUSIONS

The Pks13 model reported here provides the first structural information on the molecular mechanism of this complex enzyme and opens up new perspectives to develop inhibitors that target the interactions with its enzymatic partners or between catalytic domains within Pks13 itself.

Development of a chicken interferon-induced transmembrane protein 3 (IFITM3)-specific monoclonal antibody using phage display

Interferon-induced transmembrane protein 3 (IFITM3) has potent antiviral activity against several viruses. Recent studies have reported that the chicken IFITM3 gene also plays a pivotal role in blocking viral replication, but these studies are considerably limited due to being conducted at the RNA level only. Thus, the development of a chicken IFITM3 protein-specific antibody is needed to validate the function of IFITM3 at the protein level. Epitope prediction was performed with the immune epitope database analysis resource (IEDB-AR) program. The epitope was validated by four in silico programs, Jped4, Clustal Omega, TMpred and SOSUI. Chicken IFITM3 protein-specific monoclonal antibodies were screened by enzyme-linked immunosorbent assay through affinity between recombinant IFITM3 protein and phage-displayed candidate antibodies. Validation of the reactivity of the chicken IFITM3 protein-specific antibody to chicken tissues was carried out using western blotting. We developed a chicken IFITM3 protein-specific monoclonal antibody using phage display. The reactivity of the antibody with peripheral chicken tissues was confirmed using western blotting. To the best of our knowledge, this was the first development of a chicken IFITM3 protein-specific monoclonal antibody using phage display.

Structural Models for Roseolovirus U20 And U21: Non-Classical MHC-I Like Proteins From HHV-6A, HHV-6B, and HHV-7

Human roseolovirus U20 and U21 are type I membrane glycoproteins that have been implicated in immune evasion by interfering with recognition of classical and non-classical MHC proteins. U20 and U21 are predicted to be type I glycoproteins with extracytosolic immunoglobulin-like domains, but detailed structural information is lacking. AlphaFold and RoseTTAfold are next generation machine-learning-based prediction engines that recently have revolutionized the field of computational three-dimensional protein structure prediction. Here, we review the structural biology of viral immunoevasins and the current status of computational structure prediction algorithms. We use these computational tools to generate structural models for U20 and U21 proteins, which are predicted to adopt MHC-Ia-like folds with closed MHC platforms and immunoglobulin-like domains. We evaluate these structural models and place them within current understanding of the structural basis for viral immune evasion of T cell and natural killer cell recognition.

Reconstitution of human atlastin fusion activity reveals autoinhibition by the C terminus

ER network formation depends on membrane fusion by the atlastin (ATL) GTPase. In humans, three paralogs are differentially expressed with divergent N- and C-terminal extensions, but their respective roles remain unknown. This is partly because, unlike Drosophila ATL, the fusion activity of human ATLs has not been reconstituted. Here, we report successful reconstitution of fusion activity by the human ATLs. Unexpectedly, the major splice isoforms of ATL1 and ATL2 are each autoinhibited, albeit to differing degrees. For the more strongly inhibited ATL2, autoinhibition mapped to a C-terminal α-helix is predicted to be continuous with an amphipathic helix required for fusion. Charge reversal of residues in the inhibitory domain strongly activated its fusion activity, and overexpression of this disinhibited version caused ER collapse. Neurons express an ATL2 splice isoform whose sequence differs in the inhibitory domain, and this form showed full fusion activity. These findings reveal autoinhibition and alternate splicing as regulators of atlastin-mediated ER fusion.

Development of a Phage Cocktail to Target Salmonella Strains Associated with Swine

Infections caused by multidrug resistant Salmonella strains are problematic in swine and are entering human food chains. Bacteriophages (phages) could be used to complement or replace antibiotics to reduce infection within swine. Here, we extensively characterised six broad host range lytic Salmonella phages, with the aim of developing a phage cocktail to prevent or treat infection. Intriguingly, the phages tested differed by one to five single nucleotide polymorphisms. However, there were clear phenotypic differences between them, especially in their heat and pH sensitivity. In vitro killing assays were conducted to determine the efficacy of phages alone and when combined, and three cocktails reduced bacterial numbers by ~2 × 10³ CFU/mL within two hours. These cocktails were tested in larvae challenge studies, and prophylactic treatment with phage cocktail SPFM10-SPFM14 was the most efficient. Phage treatment improved larvae survival to 90% after 72 h versus 3% in the infected untreated group. In 65% of the phage-treated larvae, Salmonella counts were below the detection limit, whereas it was isolated from 100% of the infected, untreated larvae group. This study demonstrates that phages effectively reduce Salmonella colonisation in larvae, which supports their ability to similarly protect swine.

Combining Cryo-EM Density Map and Residue Contact for Protein Secondary Structure Topologies

Although atomic structures have been determined directly from cryo-EM density maps with high resolutions, current structure determination methods for medium resolution (5 to 10 Å) cryo-EM maps are limited by the availability of structure templates. Secondary structure traces are lines detected from a cryo-EM density map for α-helices and β-strands of a protein. A topology of secondary structures defines the mapping between a set of sequence segments and a set of traces of secondary structures in three-dimensional space. In order to enhance accuracy in ranking secondary structure topologies, we explored a method that combines three sources of information: a set of sequence segments in 1D, a set of amino acid contact pairs in 2D, and a set of traces in 3D at the secondary structure level. A test of fourteen cases shows that the accuracy of predicted secondary structures is critical for deriving topologies. The use of significant long-range contact pairs is most effective at enriching the rank of the maximum-match topology for proteins with a large number of secondary structures, if the secondary structure prediction is fairly accurate. It was observed that the enrichment depends on the quality of initial topology candidates in this approach. We provide detailed analysis in various cases to show the potential and challenge when combining three sources of information.

Increasing the accuracy of single sequence prediction methods using a deep semi-supervised learning framework

MOTIVATION

Over the past 50 years, our ability to model protein sequences with evolutionary information has progressed in leaps and bounds. However, even with the latest deep learning methods, the modelling of a critically important class of proteins, single orphan sequences, remains unsolved.

RESULTS

By taking a bioinformatics approach to semi-supervised machine learning, we develop Profile Augmentation of Single Sequences (PASS), a simple but powerful framework for building accurate single-sequence methods. To demonstrate the effectiveness of PASS we apply it to the mature field of secondary structure prediction. In doing so we develop S4PRED, the successor to the open-source PSIPRED-Single method, which achieves an unprecedented Q3 score of 75.3% on the standard CB513 test. PASS provides a blueprint for the development of a new generation of predictive methods, advancing our ability to model individual protein sequences.

AVAILABILITY AND IMPLEMENTATION

The S4PRED model is available as open source software on the PSIPRED GitHub repository (https://github.com/psipred/s4pred), along with documentation. It will also be provided as a part of the PSIPRED web service (http://bioinf.cs.ucl.ac.uk/psipred/).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An Analysis of Structure-function Co-relation between GLI Oncoprotein and HLA Immune-gene Transcriptional Regulation through Molecular Docking

Background:

GLI proteins play a significant role in the transduction of the Hedgehog (Hh) signaling pathway. A variety of human cancers, including the brain, gastrointestinal, lung, breast, and prostate cancers, demonstrate inappropriate activation of this pathway. GLI helps in proliferation and has an inhibitory role in the differentiation of hematopoietic stem cells. Malignancies may have a defect in differentiation. Different types of malignancies and undifferentiated cells have a low level of HLA expression on their cell surface.

Objective:

Human Leukocytic Antigen (HLA) downregulation is frequently observed in cancer cells. This work is aimed to hypothesize whether this downregulation of HLA molecules is GLI oncoprotein mediated or not. To understand the roles of different types of GLI oncoproteins on different classes of HLA transcriptional machinery was carried out through structure-based modeling and molecular docking studies.

Methods:

To investigate the role of GLI in HLA expression /downregulation is Hh-GLI mediated or not, molecular docking based computational interaction studies were performed between different GLI proteins (GLI1, GLI2, and GLI3) with TATA box binding protein (TBP) and compare the binding efficiencies of different HLA gene (both HLA class I and –II) regulating transcription factors (RelA, RFX5, RFXAP, RFXANK, CIITA, CREB1, and their combinations) with TBP. Due to unavailability of 3D protein structures of GLI2 and cyclin D2 (a natural ligand of GLI1) were modelled followed by structural validation by Ramachandran plot analysis.

Results:

GLI proteins especially, GLI1 and GLI2, have almost similar binding energy of RFX5-RFXANK- RFXAP and CIITA multi-protein complex to TBP but has lower binding energy between RelA to TBP.

Conclusion:

This study suggests that HLA class I may not be downregulated by GLI; however, over-expression of GLI1 is may be responsible for HLA class II downregulation. Thus this protein may be responsible for the maintenance of the undifferentiated state of malignant cells. This study also suggests the implicative role of GLI1 in the early definitive stage of hematopoiesis.

Secondary Structure of the Novel Myosin Binding Domain WYR and Implications within Myosin Structure

Structural changes in the myosin II light meromyosin (LMM) that influence thick filament mechanical properties and muscle function are modulated by LMM-binding proteins. Flightin is an LMM-binding protein indispensable for the function of Drosophila indirect flight muscle (IFM). Flightin has a three-domain structure that includes WYR, a novel 52 aa domain conserved throughout Pancrustacea. In this study, we (i) test the hypothesis that WYR binds the LMM, (ii) characterize the secondary structure of WYR, and (iii) examine the structural impact WYR has on the LMM. Circular dichroism at 260-190 nm reveals a structural profile for WYR and supports an interaction between WYR and LMM. A WYR-LMM interaction is supported by co-sedimentation with a stoichiometry of ~2.4:1. The WYR-LMM interaction results in an overall increased coiled-coil content, while curtailing ɑ helical content. WYR is found to be composed of 15% turns, 31% antiparallel β, and 48% 'other' content. We propose a structural model of WYR consisting of an antiparallel β hairpin between Q92-K114 centered on an ASX or β turn around N102, with a G1 bulge at G117. The Drosophila LMM segment used, V1346-I1941, encompassing conserved skip residues 2-4, is found to possess a traditional helical profile but is interpreted as having <30% helical content by multiple methods of deconvolution. This low helicity may be affiliated with the dynamic behavior of the structure in solution or the inclusion of a known non-helical region in the C-terminus. Our results support the hypothesis that WYR binds the LMM and that this interaction brings about structural changes in the coiled-coil. These studies implicate flightin, via the WYR domain, for distinct shifts in LMM secondary structure that could influence the structural properties and stabilization of the thick filament, scaling to modulation of whole muscle function.

Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction

The new advances in deep learning methods have influenced many aspects of scientific research, including the study of the protein system. The prediction of proteins' 3D structural components is now heavily dependent on machine learning techniques that interpret how protein sequences and their homology govern the inter-residue contacts and structural organization. Especially, methods employing deep neural networks have had a significant impact on recent CASP13 and CASP14 competition. Here, we explore the recent applications of deep learning methods in the protein structure prediction area. We also look at the potential opportunities for deep learning methods to identify unknown protein structures and functions to be discovered and help guide drug-target interactions. Although significant problems still need to be addressed, we expect these techniques in the near future to play crucial roles in protein structural bioinformatics as well as in drug discovery.

Systemic Expression, Purification, and Initial Structural Characterization of Bacteriophage T4 Proteins Without Known Structure Homologs

Bacteriophage T4 of Escherichia coli is one of the most studied phages. Research into it has led to numerous contributions to phage biology and biochemistry. Coding about 300 gene products, this double-stranded DNA virus is the best-understood model in phage study and modern genomics and proteomics. Ranging from viral RNA polymerase, commonly found in phages, to thymidylate synthase, whose mRNA requires eukaryotic-like self-splicing, its gene products provide a pool of fine examples for phage research. However, there are still up to 130 gene products that remain poorly characterized despite being one of the most-studied model phages. With the recent advancement of cryo-electron microscopy, we have a glimpse of the virion and the structural proteins that present in the final assembly. Unfortunately, proteins participating in other stages of phage development are absent. Here, we report our systemic analysis on 22 of these structurally uncharacterized proteins, of which none has a known homologous structure due to the low sequence homology to published structures and does not belong to the category of viral structural protein. Using NMR spectroscopy and cryo-EM, we provided a set of preliminary structural information for some of these proteins including NMR backbone assignment for Cef. Our findings pave the way for structural determination for the phage proteins, whose sequences are mainly conserved among phages. While this work provides the foundation for structural determinations of proteins like Gp57B, Cef, Y04L, and Mrh, other in vitro studies would also benefit from the high yield expression of these proteins.

Applications of Protein Secondary Structure Algorithms in SARS-CoV-2 Research

Since the outset of COVID-19, the pandemic has prompted immediate global efforts to sequence SARS-CoV-2, and over 450 000 complete genomes have been publicly deposited over the course of 12 months. Despite this, comparative nucleotide and amino acid sequence analyses often fall short in answering key questions in vaccine design. For example, the binding affinity between different ACE2 receptors and SARS-COV-2 spike protein cannot be fully explained by amino acid similarity at ACE2 contact sites because protein structure similarities are not fully reflected by amino acid sequence similarities. To comprehensively compare protein homology, secondary structure (SS) analysis is required. While protein structure is slow and difficult to obtain, SS predictions can be made rapidly, and a well-predicted SS structure may serve as a viable proxy to gain biological insight. Here we review algorithms and information used in predicting protein SS to highlight its potential application in pandemics research. We also showed examples of how SS predictions can be used to compare ACE2 proteins and to evaluate the zoonotic origins of viruses. As computational tools are much faster than wet-lab experiments, these applications can be important for research especially in times when quickly obtained biological insights can help in speeding up response to pandemics.

Bioinformatics Analysis of SARS-CoV-2 to Approach an Effective Vaccine Candidate Against COVID-19

The emerging Coronavirus Disease 2019 (COVID-19) pandemic has posed a serious threat to the public health worldwide, demanding urgent vaccine provide. According to the virus feature as an RNA virus, a high rate of mutations imposes some vaccine design difficulties. Bioinformatics tools have been widely used to make advantage of conserved regions as well as immunogenicity. In this study, we aimed at immunoinformatic evaluation of SARS-CoV-2 proteins conservancy and immunogenicity to design a preventive vaccine candidate. Spike, Membrane and Nucleocapsid amino acid sequences were obtained, and four possible fusion proteins were assessed and compared in terms of structural features and immunogenicity, and population coverage. MHC-I and MHC-II T-cell epitopes, the linear and conformational B-cell epitopes were evaluated. Among the predicted models, the truncated form of Spike in fusion with M and N protein applying AAY linker has high rate of MHC-I and MCH-II epitopes with high antigenicity and acceptable population coverage of 82.95% in Iran and 92.51% in Europe. The in silico study provided truncated Spike-M-N SARS-CoV-2 as a potential preventive vaccine candidate for further in vivo evaluation.

RovC - a novel type of hexameric transcriptional activator promoting type VI secretion gene expression

Type VI secretion systems (T6SSs) are complex macromolecular injection machines which are widespread in Gram-negative bacteria. They are involved in host-cell interactions and pathogenesis, required to eliminate competing bacteria, or are important for the adaptation to environmental stress conditions. Here we identified regulatory elements controlling the T6SS4 of Yersinia pseudotuberculosis and found a novel type of hexameric transcription factor, RovC. RovC directly interacts with the T6SS4 promoter region and activates T6SS4 transcription alone or in cooperation with the LysR-type regulator RovM. A higher complexity of regulation was achieved by the nutrient-responsive global regulator CsrA, which controls rovC expression on the transcriptional and post-transcriptional level. In summary, our work unveils a central mechanism in which RovC, a novel key activator, orchestrates the expression of the T6SS weapons together with a global regulator to deploy the system in response to the availability of nutrients in the species' native environment.

Plant Nitrilase Homologues in Fungi: Phylogenetic and Functional Analysis with Focus on Nitrilases in Trametes versicolor and Agaricus bisporus

Fungi contain many plant-nitrilase (NLase) homologues according to database searches. In this study, enzymes NitTv1 from Trametes versicolor and NitAb from Agaricus bisporus were purified and characterized as the representatives of this type of fungal NLase. Both enzymes were slightly more similar to NIT4 type than to NIT1/NIT2/NIT3 type of plant NLases in terms of their amino acid sequences. Expression of the synthetic genes in Escherichia coli Origami B (DE3) was induced with 0.02 mM isopropyl β-D-1-thiogalactopyranoside at 20 °C. Purification of NitTv1 and NitAb by cobalt affinity chromatography gave ca. 6.6 mg and 9.6 mg of protein per 100 mL of culture medium, respectively. Their activities were determined with 25 mM of nitriles in 50 mM Tris/HCl buffer, pH 8.0, at 30 °C. NitTv1 and NitAb transformed β-cyano-L-alanine (β-CA) with the highest specific activities (ca. 132 and 40 U mg⁻¹, respectively) similar to plant NLase NIT4. β-CA was transformed into Asn and Asp as in NIT4 but at lower Asn:Asp ratios. The fungal NLases also exhibited significant activities for (aryl)aliphatic nitriles such as 3-phenylpropionitrile, cinnamonitrile and fumaronitrile (substrates of NLase NIT1). NitTv1 was more stable than NitAb (at pH 5–9 vs. pH 5–7). These NLases may participate in plant–fungus interactions by detoxifying plant nitriles and/or producing plant hormones. Their homology models elucidated the molecular interactions with various nitriles in their active sites.

Distinct Calcium Binding and Structural Properties of Two Centrin Isoforms from Toxoplasma gondii

Centrins are calcium (Ca2+)-binding proteins that have been implicated in several regulatory functions. In the protozoan parasite Toxoplasma gondii, the causative agent of toxoplasmosis, three isoforms of centrin have been identified. While increasing information is now available that links the function of centrins with defined parasite biological processes, knowledge is still limited on the metal-binding and structural properties of these proteins. Herein, using biophysical and structural approaches, we explored the Ca2+ binding abilities and the subsequent effects of Ca2+ on the structure of a conserved (TgCEN1) and a more divergent (TgCEN2) centrin isoform from T. gondii. Our data showed that TgCEN1 and TgCEN2 possess diverse molecular features, suggesting that they play nonredundant roles in parasite physiology. TgCEN1 binds two Ca2+ ions with high/medium affinity, while TgCEN2 binds one Ca2+ with low affinity. TgCEN1 undergoes significant Ca2+-dependent conformational changes that expose hydrophobic patches, supporting a role as a Ca2+ sensor in toxoplasma. In contrast, Ca2+ binding has a subtle influence on conformational features of TgCEN2 without resulting in hydrophobic exposure, suggesting a different Ca2+ relay mode for this isoform. Furthermore, TgCEN1 displays a Ca2+-dependent ability to self-assemble, while TgCEN2 did not. We discuss our findings in the context of Ca2+ signaling in toxoplasma.

Structural profile matrices for predicting structural properties of proteins

Predicting structural properties of proteins plays a key role in predicting the 3D structure of proteins. In this study, new structural profile matrices (SPM) are developed for protein secondary structure, solvent accessibility and torsion angle class predictions, which could be used as input to 3D prediction algorithms. The structural templates employed in computing SPMs are detected by eight alignment methods in LOMETS server, gap affine alignment method, ScanProsite, PfamScan, and HHblits. The contribution of each template is weighted by its similarity to target, which is assessed by several sequence alignment scores. For comparison, the SPMs are also computed using Homolpro, which uses BLAST for target template alignments and does not assign weights to templates. Incorporating the SPMs into DSPRED classifier, the prediction accuracy improves significantly as demonstrated by cross-validation experiments on two difficult benchmarks. The most accurate predictions are obtained using the SPMs derived by threading methods in LOMETS server. On the other hand, the computational cost of computing these SPMs was the highest.

Multifaceted analysis of training and testing convolutional neural networks for protein secondary structure prediction

Protein secondary structure prediction remains a vital topic with broad applications. Due to lack of a widely accepted standard in secondary structure predictor evaluation, a fair comparison of predictors is challenging. A detailed examination of factors that contribute to higher accuracy is also lacking. In this paper, we present: (1) new test sets, Test2018, Test2019, and Test2018-2019, consisting of proteins from structures released in 2018 and 2019 with less than 25% identity to any protein published before 2018; (2) a 4-layer convolutional neural network, SecNet, with an input window of ±14 amino acids which was trained on proteins ≤25% identical to proteins in Test2018 and the commonly used CB513 test set; (3) an additional test set that shares no homologous domains with the training set proteins, according to the Evolutionary Classification of Proteins (ECOD) database; (4) a detailed ablation study where we reverse one algorithmic choice at a time in SecNet and evaluate the effect on the prediction accuracy; (5) new 4- and 5-label prediction alphabets that may be more practical for tertiary structure prediction methods. The 3-label accuracy (helix, sheet, coil) of the leading predictors on both Test2018 and CB513 is 81-82%, while SecNet's accuracy is 84% for both sets. Accuracy on the non-homologous ECOD set is only 0.6 points (83.9%) lower than the results on the Test2018-2019 set (84.5%). The ablation study of features, neural network architecture, and training hyper-parameters suggests the best accuracy results are achieved with good choices for each of them while the neural network architecture is not as critical as long as it is not too simple. Protocols for generating and using unbiased test, validation, and training sets are provided. Our data sets, including input features and assigned labels, and SecNet software including third-party dependencies and databases, are downloadable from dunbrack.fccc.edu/ss and github.com/sh-maxim/ss.

Homology Modeling and Virtual Screening Studies of Antigen MLAA-42 Protein: Identification of Novel Drug Candidates against Leukemia-An In Silico Approach

Monocytic leukemia-associated antigen-42 (MLAA-42) is associated with excessive cell division and progression of leukemia. Thus, human MLAA-42 is considered as a promising target for designing of new lead molecules for leukemia treatment. Herein, the 3D model of the target was generated by homology modeling technique. The model was then evaluated using various cheminformatics servers. Moreover, the virtual screening studies were performed to explore the possible binding patterns of ligand molecules to MLAA's active site pocket. Thirteen ligand molecules from the ChemBank™ database were identified as they showed good binding affinities, scaffold diversity, and preferential ADME properties which may act as potent drug candidates against leukemia. The study provides the way to identify novel therapeutics with optimal efficacy, targeting MLAA-42.

Evolutionary Switches Structural Transitions via Coarse-Grained Models

Transitions between different conformational states are ubiquitous in proteins. A vast class of conformation-changing proteins includes evolutionary switches, which vary their conformation as an effect of few mutations or weak environmental variations. However, modeling those processes is extremely difficult due to the need of efficiently exploring a vast conformational space to look for the actual transition path. In this study, we report a strategy that simplifies this task attacking the complexity on several sides. We first apply a minimalist coarse-grained model to the protein, based on an empirical force field with a partial structural bias toward one or both the reference structures. We then explore the transition paths by means of stochastic molecular dynamics and select representative structures by means of a principal path-based clustering algorithm. We finally compare this trajectory with that produced by independent methods adopting a morphing-oriented approach. Our analysis indicates that the minimalist model returns trajectories capable of exploring intermediate states with physical meaning, retaining a very low computational cost, which can allow systematic and extensive exploration of the multistable proteins transition pathways.

An evaluation of different classification algorithms for protein sequence-based reverse vaccinology prediction

Previous work has shown that proteins that have the potential to be vaccine candidates can be predicted from features derived from their amino acid sequences. In this work, we make an empirical comparison across various machine learning classifiers on this sequence-based inference problem. Using systematic cross validation on a dataset of 200 known vaccine candidates and 200 negative examples, with a set of 525 features derived from the AA sequences and feature selection applied through a greedy backward elimination approach, we show that simple classification algorithms often perform as well as more complex support vector kernel machines. The work also includes a novel cross validation applied across bacterial species, i.e. the validation proteins all come from a specific species of bacterium not represented in the training set. We termed this type of validation Leave One Bacteria Out Validation (LOBOV).

Competition of individual domain folding with inter-domain interaction in WW domain engineered repeat proteins

Engineered repeat proteins have proven to be a fertile ground for studying the competition between folding, misfolding and transient aggregation of tethered protein domains. We examine the interplay between folding and inter-domain interactions of engineered FiP35 WW domain repeat proteins with n = 1 through 5 repeats. We characterize protein expression, thermal and guanidium melts, as well as laser T-jump kinetics. All experimental data is fitted by a global fitting model with two states per domain (U, N), plus a third state M to account for non-native states due to domain interactions present in all but the monomer. A detailed structural model is provided by coarse-grained simulated annealing using the AWSEM Hamiltonian. Tethered FiP35 WW domains with n = 2 and 3 domains are just slightly less stable than the monomer. The n = 4 oligomer is yet less stable, its expression yield is much lower than the monomer's, and depends on the purification tag used. The n = 5 plasmid did not express at all, indicating the sudden onset of aggregation past n = 4. Thus, tethered FiP35 has a critical nucleus size for inter-domain aggregation of n ≈ 4. According to our simulations, misfolded structures become increasingly prevalent as one proceeds from monomer to pentamer, with extended inter-domain beta sheets appearing first, then multi-sheet 'intramolecular amyloid' structures, and finally novel motifs containing alpha helices. We discuss the implications of our results for oligomeric aggregate formation and structure, transient aggregation of proteins whilst folding, as well as for protein evolution that starts with repeat proteins.

Crystal structure of TbEsa1 presumed Tudor domain from Trypanosoma brucei

The essential SAS2-related acetyltransferase 1 (Esa1), as a acetyltransferase of MYST family, is indispensable for the cell cycle and transcriptional regulation. The Tudor domain consists of 60 amino acids and belongs to the Royal family, which serves as a module interacting with methylated histone and/or DNA. Although Tudor domain has been widely studied in higher eukaryotes, its structure and function remain unclear in Trypanosoma brucei (T. brucei), a protozoan unicellular parasite causing sleeping sickness in human and nagana in cattle in sub-Saharan Africa. Here, we determined a high-resolution structure of TbEsa1 presumed Tudor domain from T. brucei by X-ray crystallography. TbEsa1 Tudor domain adopts a conserved Tudor-like fold, which is comprised of a five-stranded β-barrel surrounded by two short α-helices. Furthermore, we revealed a non-specific DNA binding pattern of TbEsa1 Tudor domain. However, TbEsa1 Tudor domain showed no methyl-histone binding ability, due to the absence of key aromatic residues forming a conserved aromatic cage.

Expression and Interactions of Kinetoplastid Kinetochore Proteins (KKTs) from Trypanosoma brucei

Background:

Kinetochores are the macromolecular protein complex that drives chromosome segregation by interacting with centromeric DNA and spindle microtubules in eukaryotes. Kinetochores in well studied eukaryotes bind DNA through widely conserved components like Centromere Protein (CENP)-A and bind microtubules through the Ndc80 complex. However, unconventional type of kinetochore proteins (KKT1-20) were identified in evolutionarily divergent kinetoplastid species such as Trypanosoma brucei (T. brucei), indicating that chromosome segregation is driven by a distinct set of proteins. KKT proteins are comprised of sequential α-helixes that tend to form coiled-coil structures, which will further lead to polymerization and misfolding of proteins, resulting in the formation of inclusion bodies.

Results and Conclusion:

We expressed and purified the stable KKT proteins with Maltose Binding Protein (MBP) fusion tag in E. coli or Protein A tag in Human Embryonic Kidney (HEK) 293T cells. Furthermore, we identified interactions among KKT proteins using yeast two-hybrid system. The study provides an important basis for further better understanding of the structure and function of KKT proteins.

Recent developments in deep learning applied to protein structure prediction

Although many structural bioinformatics tools have been using neural network models for a long time, deep neural network (DNN) models have attracted considerable interest in recent years. Methods employing DNNs have had a significant impact in recent CASP experiments, notably in CASP12 and especially CASP13. In this article, we offer a brief introduction to some of the key principles and properties of DNN models and discuss why they are naturally suited to certain problems in structural bioinformatics. We also briefly discuss methodological improvements that have enabled these successes. Using the contact prediction task as an example, we also speculate why DNN models are able to produce reasonably accurate predictions even in the absence of many homologues for a given target sequence, a result that can at first glance appear surprising given the lack of input information. We end on some thoughts about how and why these types of models can be so effective, as well as a discussion on potential pitfalls.

The catalytic core of DEMETER guides active DNA demethylation in Arabidopsis

The Arabidopsis DEMETER (DME) DNA glycosylase demethylates the maternal genome in the central cell prior to fertilization and is essential for seed viability. DME preferentially targets small transposons that flank coding genes, influencing their expression and initiating plant gene imprinting. DME also targets intergenic and heterochromatic regions, but how it is recruited to these differing chromatin landscapes is unknown. The C-terminal half of DME consists of 3 conserved regions required for catalysis in vitro. We show that this catalytic core guides active demethylation at endogenous targets, rescuing dme developmental and genomic hypermethylation phenotypes. However, without the N terminus, heterochromatin demethylation is significantly impeded, and abundant CG-methylated genic sequences are ectopically demethylated. Comparative analysis revealed that the conserved DME N-terminal domains are present only in flowering plants, whereas the domain architecture of DME-like proteins in nonvascular plants mainly resembles the catalytic core, suggesting that it might represent the ancestral form of the 5mC DNA glycosylase found in plant lineages. We propose a bipartite model for DME protein action and suggest that the DME N terminus was acquired late during land plant evolution to improve specificity and facilitate demethylation at heterochromatin targets.

rawMSA: End-to-end Deep Learning using raw Multiple Sequence Alignments

In the last decades, huge efforts have been made in the bioinformatics community to develop machine learning-based methods for the prediction of structural features of proteins in the hope of answering fundamental questions about the way proteins function and their involvement in several illnesses. The recent advent of Deep Learning has renewed the interest in neural networks, with dozens of methods being developed taking advantage of these new architectures. However, most methods are still heavily based pre-processing of the input data, as well as extraction and integration of multiple hand-picked, and manually designed features. Multiple Sequence Alignments (MSA) are the most common source of information in de novo prediction methods. Deep Networks that automatically refine the MSA and extract useful features from it would be immensely powerful. In this work, we propose a new paradigm for the prediction of protein structural features called rawMSA. The core idea behind rawMSA is borrowed from the field of natural language processing to map amino acid sequences into an adaptively learned continuous space. This allows the whole MSA to be input into a Deep Network, thus rendering pre-calculated features such as sequence profiles and other features calculated from MSA obsolete. We showcased the rawMSA methodology on three different prediction problems: secondary structure, relative solvent accessibility and inter-residue contact maps. We have rigorously trained and benchmarked rawMSA on a large set of proteins and have determined that it outperforms classical methods based on position-specific scoring matrices (PSSM) when predicting secondary structure and solvent accessibility, while performing on par with methods using more pre-calculated features in the inter-residue contact map prediction category in CASP12 and CASP13. Clearly demonstrating that rawMSA represents a promising development that can pave the way for improved methods using rawMSA instead of sequence profiles to represent evolutionary information in the coming years. Availability: datasets, dataset generation code, evaluation code and models are available at: https://bitbucket.org/clami66/rawmsa.

In Silico Design and Immunologic Evaluation of HIV-1 p24-Nef Fusion Protein to Approach a Therapeutic Vaccine Candidate

Background:

Acquired immune deficiency syndrome (HIV/AIDS) has been a major glob-al health concern for over 38 years. No safe and effective preventive or therapeutic vaccine has been developed although many products have been investigated. Computational methods have facilitated vaccine developments in recent decades. Among HIV-1 proteins, p24 and Nef are two suitable targets to provoke the cellular immune response. However, the fusion form of these two proteins has not been analyzed in silico yet.

Objective:

This study aimed at the evaluation of possible fusion forms of p24 and Nef in order to achieve a potential therapeutic subunit vaccine against HIV-1.

Method:

In this study, various computational approaches have been applied to predict the most effec-tive fusion form of p24-Nef including CTL (Cytotoxic T lymphocytes) response, immunogenicity, conservation and population coverage. Moreover, binding to MHC (Major histocompatibility com-plex) molecules was assessed in both human and BALB/c.

Results:

After analyzing six possible fusion protein forms using AAY linker, we came up with the most practical form of p24 from 80 to 231 and Nef from 120 to 150 regions (according to their refer-ence sequence of HXB2 strain) using an AAY linker, based on their peptides affinity to MHC mole-cules which are located in a conserved region among different virus clades. The selected fusion protein contains seventeen MHC I antigenic epitopes, among them KRWIILGLN, YKRWIILGL, DIAG-TTSTL and FPDWQNYTP are fully conserved between the virus clades. Furthermore, analyzed class I CTL epitopes showed greater affinity binding to HLA-B 57*01, HLA-B*51:01 and HLA-B 27*02 molecules. The population coverage with the rate of >70% coverage in the Persian population supports this truncated form as an appropriate candidate against HIV-I virus.

Conclusion:

The predicted fusion protein, p24-AAY-Nef in a truncated form with a high rate of T cell epitopes and high conservancy rate among different clades, provides a helpful model for developing a therapeutic vaccine candidate against HIV-1.

Characterization of the Propham Biodegradation Pathway in Starkeya sp. Strain YW6 and Cloning of a Novel Amidase Gene mmH

We previously isolated a monocrotophos-degrading strain Starkeya sp. YW6, which could also degrade propham. Here, we show that strain YW6 metabolizes propham via a pathway in which propham is initially hydrolyzed to aniline and then converted to catechol, which is then oxidized via an ortho-cleavage pathway. The novel amidase gene mmH was cloned from strain YW6 and expressed in Escherichia coli BL21(DE3). MmH, which exhibits aryl acylamidase activity, was purified for enzymatic analysis. Bioinformatic analysis confirmed that MmH belongs to the amidase signature (AS) enzyme family and shares 26-50% identity with several AS family members. MmH (molecular mass of 53 kDa) was most active at 40 °C and pH 8.0 and showed high activity toward propham, with K_cat and K_m values of 33.4 s^-1 and 16.9 μM, respectively. These characteristics make MmH suitable for novel amide biosynthesis and environmental remediation.

NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning

The ability to predict local structural features of a protein from the primary sequence is of paramount importance for unraveling its function in absence of experimental structural information. Two main factors affect the utility of potential prediction tools: their accuracy must enable extraction of reliable structural information on the proteins of interest, and their runtime must be low to keep pace with sequencing data being generated at a constantly increasing speed. Here, we present NetSurfP-2.0, a novel tool that can predict the most important local structural features with unprecedented accuracy and runtime. NetSurfP-2.0 is sequence-based and uses an architecture composed of convolutional and long short-term memory neural networks trained on solved protein structures. Using a single integrated model, NetSurfP-2.0 predicts solvent accessibility, secondary structure, structural disorder, and backbone dihedral angles for each residue of the input sequences. We assessed the accuracy of NetSurfP-2.0 on several independent test datasets and found it to consistently produce state-of-the-art predictions for each of its output features. We observe a correlation of 80% between predictions and experimental data for solvent accessibility, and a precision of 85% on secondary structure 3-class predictions. In addition to improved accuracy, the processing time has been optimized to allow predicting more than 1000 proteins in less than 2 hours, and complete proteomes in less than 1 day.

Structure of an Influenza A virus N9 neuraminidase with a tetrabrachion-domain stalk

The influenza neuraminidase (NA) is a homotetramer with head, stalk, transmembrane and cytoplasmic regions. The structure of the NA head with a stalk has never been determined. The NA head from an N9 subtype influenza A virus, A/tern/Australia/G70C/1975 (H1N9), was expressed with an artificial stalk derived from the tetrabrachion (TB) tetramerization domain from Staphylothermus marinus. The NA was successfully crystallized both with and without the TB stalk, and the structures were determined to 2.6 and 2.3 Å resolution, respectively. Comparisons of the two NAs with the native N9 NA structure from egg-grown virus showed that the artificial TB stalk maintained the native NA head structure, supporting previous biological observations.

Integrated transcriptomic and proteomic analyses of a molecular mechanism of radular teeth biomineralization in Cryptochiton stelleri

Many species of chiton are known to deposit magnetite (Fe₃O₄) within the cusps of their heavily mineralized and ultrahard radular teeth. Recently, much attention has been paid to the ultrastructural design and superior mechanical properties of these radular teeth, providing a promising model for the development of novel abrasion resistant materials. Here, we constructed de novo assembled transcripts from the radular tissue of C. stelleri that were used for transcriptome and proteome analysis. Transcriptomic analysis revealed that the top 20 most highly expressed transcripts in the non-mineralized teeth region include the transcripts encoding ferritin, while those in the mineralized teeth region contain a high proportion of mitochondrial respiratory chain proteins. Proteomic analysis identified 22 proteins that were specifically expressed in the mineralized cusp. These specific proteins include a novel protein that we term radular teeth matrix protein1 (RTMP1), globins, peroxidasins, antioxidant enzymes and a ferroxidase protein. This study reports the first de novo transcriptome assembly from C. stelleri, providing a broad overview of radular teeth mineralization. This new transcriptomic resource and the proteomic profiles of mineralized cusp are valuable for further investigation of the molecular mechanisms of radular teeth mineralization in chitons.