A comparative assessment and analysis of 20 representative sequence alignment methods for protein structure prediction

CSSP-2.0: A refined consensus method for accurate protein secondary structure prediction

Studying the relationship between sequences and their corresponding three-dimensional structure assists structural biologists in solving the protein-folding problem. Despite several experimental and in-silico approaches, still understanding or decoding the three-dimensional structures from the sequence remains a mystery. In such cases, the accuracy of the structure prediction plays an indispensable role. To address this issue, an updated web server (CSSP-2.0) has been created to improve the accuracy of our previous version of CSSP by deploying the existing algorithms. It uses input as probabilities and predicts the consensus for the secondary structure as a highly accurate three-state Q3 (helix, strand, and coil). This prediction is achieved using six recent top-performing methods: MUFOLD-SS, RaptorX, PSSpred v4, PSIPRED, JPred v4, and Porter 5.0. CSSP-2.0 validation includes datasets involving various protein classes from the PDB, CullPDB, and AlphaFold databases. Our results indicate a significant improvement in the accuracy of the consensus Q3 prediction. Using CSSP-2.0, crystallographers can sort out the stable regular secondary structures from the entire complex structure, which would aid in inferring the functional annotation of hypothetical proteins. The web server is freely available at https://bioserver3.physics.iisc.ac.in/cgi-bin/cssp-2/.

Molecular dynamics simulation of the brain-isolated single-domain antibody/nanobody from camels through in vivo phage display screening

Introduction

During the last decade, there has been a significant rise in the use of therapeutic antibodies or passive immunotherapy for treating various conditions like inflammation and cancer. However, these proteins face challenges reaching the brain and often require specialized delivery methods such as single-domain antibodies (sdAbs). Traditional antibodies struggle to efficiently cross the blood-brain barrier (BBB), hindering their effectiveness. Receptor-mediated transcytosis (RMT) offers a promising pathway for transporting large molecules essential for brain function and treatment across the BBB.

Methods

SdAbs and peptide ligands with an affinity for RMT receptors are commonly employed to enhance the transport of biotherapeutics compounds across the BBB. This research used a sdAbs phage-displayed library from 13 camelus dromedarius samples to identify sdABs that specifically bind to and are internalized by human BBB endothelial cells (ECs) through in vivo panning.

Results and discussion

One sdAb, defined as FB24, was isolated, sequenced, translated into an open reading frame (ORF), and subjected to three-dimensional (3D) modeling. Molecular docking and molecular dynamics simulations were carried out by the HADDOCK web server and GROMACS, respectively, to evaluate the interaction between FB24 and EC receptors in silico. The docking results revealed that FB24 exhibited binding activity against potential EC receptors with -1.7 to -2.7 ranged z score and maintained a stable structure. The docked complex of FB24-RAGE (receptor for advanced glycation end products, also known as advanced glycation end product receptor [AGER]) showed 18 hydrogen bonds and 213 non-bonded contacts. It was chosen for further analysis by molecular dynamics simulations by GROMACS. This complex showed a stable condition, and its root mean square deviation (RMSD) was 0.218 nm. The results suggest that FB24 could serve as a suitable carrier vector for transporting therapeutic and diagnostic agents across the BBB to the brain through a non-invasive route.

Disorder-to-order active site capping regulates the rate-limiting step of the inositol pathway

Myo-inositol-1-phosphate synthase (MIPS) catalyzes the NAD+-dependent isomerization of glucose-6-phosphate (G6P) into inositol-1-phosphate (IMP), controlling the rate-limiting step of the inositol pathway. Previous structural studies focused on the detailed molecular mechanism, neglecting large-scale conformational changes that drive the function of this 240 kDa homotetrameric complex. In this study, we identified the active, endogenous MIPS in cell extracts from the thermophilic fungus Thermochaetoides thermophila. By resolving the native structure at 2.48 Å (FSC = 0.143), we revealed a fully populated active site. Utilizing 3D variability analysis, we uncovered conformational states of MIPS, enabling us to directly visualize an order-to-disorder transition at its catalytic center. An acyclic intermediate of G6P occupied the active site in two out of the three conformational states, indicating a catalytic mechanism where electrostatic stabilization of high-energy intermediates plays a crucial role. Examination of all isomerases with known structures revealed similar fluctuations in secondary structure within their active sites. Based on these findings, we established a conformational selection model that governs substrate binding and eventually inositol availability. In particular, the ground state of MIPS demonstrates structural configurations regardless of substrate binding, a pattern observed across various isomerases. These findings contribute to the understanding of MIPS structure-based function, serving as a template for future studies targeting regulation and potential therapeutic applications.

Bioinformatics approach for structure modeling, vaccine design, and molecular docking of Brucella candidate proteins BvrR, OMP25, and OMP31

Brucellosis is a zoonotic disease with significant economic and healthcare costs. Despite the eradication efforts, the disease persists. Vaccines prevent disease in animals while antibiotics cure humans with limitations. This study aims to design vaccines and drugs for brucellosis in animals and humans, using protein modeling, epitope prediction, and molecular docking of the target proteins (BvrR, OMP25, and OMP31). Tertiary structure models of three target proteins were constructed and assessed using RMSD, TM-score, C-score, Z-score, and ERRAT. The best models selected from AlphaFold and I-TASSER due to their superior performance according to CASP 12 - CASP 15 were chosen for further analysis. The motif analysis of best models using MotifFinder revealed two, five, and five protein binding motifs, however, the Motif Scan identified seven, six, and eight Post-Translational Modification sites (PTMs) in the BvrR, OMP25, and OMP31 proteins, respectively. Dominant B cell epitopes were predicted at (44-63, 85-93, 126-137, 193-205, and 208-237), (26-46, 52-71, 98-114, 142-155, and 183-200), and (29-45, 58-82, 119-142, 177-198, and 222-251) for the three target proteins. Additionally, cytotoxic T lymphocyte epitopes were detected at (173-181, 189-197, and 202-210), (61-69, 91-99, 159-167, and 181-189), and (3-11, 24-32, 167-175, and 216-224), while T helper lymphocyte epitopes were displayed at (39-53, 57-65, 150-158, 163-171), (79-87, 95-108, 115-123, 128-142, and 189-197), and (39-47, 109-123, 216-224, and 245-253), for the respective target protein. Furthermore, structure-based virtual screening of the ZINC and DrugBank databases using the docking MOE program was followed by ADMET analysis. The best five compounds of the ZINC database revealed docking scores ranged from (- 16.8744 to - 15.1922), (- 16.0424 to - 14.1645), and (- 14.7566 to - 13.3222) for the BvrR, OMP25, and OMP31, respectively. These compounds had good ADMET parameters and no cytotoxicity, while DrugBank compounds didn't meet Lipinski's rule criteria. Therefore, the five selected compounds from the ZINC20 databases may fulfill the pharmacokinetics and could be considered lead molecules for potentially inhibiting Brucella's proteins.

Babesia bovis RON2 binds to bovine erythrocytes through a highly conserved epitope

B. bovis invasion of bovine erythrocytes requires tight junction formation involving AMA-1/RON2 complex interaction. RON2 has been considered a vaccine candidate since antibodies targeting the protein can inhibit parasite invasion of target cells; however, the mechanism controlling B. bovis RON2 interaction with red blood cells is not yet fully understood. This study was thus aimed at identifying B. bovis RON2 protein regions associated with interaction with bovine erythrocytes. Natural selection analysis of the ron2 gene identified predominantly negative selection signals in the C-terminal region. Interestingly, protein-cell and competition assays highlighted the RON2-C region's role in peptide 42918-mediated erythrocyte binding, probably to a sialoglycoprotein receptor. This peptide (1218SFIMVKPPALHCVLKPVETL1237) lies within an intrinsically disordered region of the RON2 secondary structure flanked by two helical residues. The study provides, for the first time, valuable insights into RON2's role in interaction with its target cells. Future studies are required for studying the peptide's potential as an anti-B. bovis vaccine component.

Acceleration of Protein Degradation by 20S Proteasome-Binding Peptides Generated by In Vitro Artificial Evolution

Although the 20S core particle (CP) of the proteasome is an important component of the 26S holoenzyme, the stand-alone 20S CP acts directly on intrinsically disordered and oxidized/damaged proteins to degrade them in a ubiquitin-independent manner. It has been postulated that some structural features of substrate proteins are recognized by the 20S CP to promote substrate uptake, but the mechanism of substrate recognition has not been fully elucidated. In this study, we screened peptides that bind to the 20S CP from a random eight-residue pool of amino acid sequences using complementary DNA display an in vitro molecular evolution technique. The identified 20S CP-binding amino acid sequence was chemically synthesized and its effects on the 20S CP were investigated. The 20S CP-binding peptide stimulated the proteolytic activity of the inactive form of 20S CP. The peptide bound directly to one of the α-subunits, opening a gate for substrate entry on the α-ring. Furthermore, the attachment of this peptide sequence to α-synuclein enhanced its degradation by the 20S CP in vitro. In addition to these results, docking simulations indicated that this peptide binds to the top surface of the α-ring. These peptides could function as a key to control the opening of the α-ring gate.

zPoseScore model for accurate and robust protein-ligand docking pose scoring in CASP15

We introduce a deep learning-based ligand pose scoring model called zPoseScore for predicting protein-ligand complexes in the 15th Critical Assessment of Protein Structure Prediction (CASP15). Our contributions are threefold: first, we generate six training and evaluation data sets by employing advanced data augmentation and sampling methods. Second, we redesign the "zFormer" module, inspired by AlphaFold2's Evoformer, to efficiently describe protein-ligand interactions. This module enables the extraction of protein-ligand paired features that lead to accurate predictions. Finally, we develop the zPoseScore framework with zFormer for scoring and ranking ligand poses, allowing for atomic-level protein-ligand feature encoding and fusion to output refined ligand poses and ligand per-atom deviations. Our results demonstrate excellent performance on various testing data sets, achieving Pearson's correlation R  = 0.783 and 0.659 for ranking docking decoys generated based on experimental and predicted protein structures of CASF-2016 protein-ligand complexes. Additionally, we obtain an averaged local distance difference test (lDDT pli = 0.558) of AIchemy LIG2 in CASP15 for de novo protein-ligand complex structure predictions. Detailed analysis shows that accurate ligand binding site prediction and side-chain orientation are crucial for achieving better prediction performance. Our proposed model is one of the most accurate protein-ligand pose prediction models and could serve as a valuable tool in small molecule drug discovery.

Functional mimicry of sea urchin biomineralization proteins with CaCO3-binding peptides selected by phage display

The intricate process of biomineralization, e.g. in sea urchins, involves the precise interplay of highly regulated mineralization proteins and the spatiotemporal coordination achieved through compartmentalization. However, the investigation of biomineralization effector molecules, e.g. proteins, is challenging, due to their very low abundance. Therefore, we investigate the functional mimicry in the bioinspired precipitation of calcium carbonate (CaCO3) with artificial peptides selected from a peptide library by phage display based on peptide-binding to calcite and aragonite, respectively. The structure-directing effects of the identified peptides were compared to those of natural protein mixes isolated from skeletal (test) structures of two sea urchin species (Arbacia lixula and Paracentrotus lividus). The calcium carbonate samples deposited in the absence or presence of peptides were analyzed with a set of complementary techniques with regard to morphology, polymorph, and nanostructural motifs. Remarkably, some of the CaCO3-binding peptides induced morphological features in calcite that appeared similar to those obtained in the presence of the natural protein mixes. Many of the peptides identified as most effective in exerting a structure-directing effect on calcium carbonate crystallization were rich in basic amino acid residues. Hence, our in vitro mineralization study further highlights the important, but often neglected, role of positively charged soluble organic matrices associated with biological and bioinspired CaCO3 deposition.

Obtaining high yield recombinant Enterococcus faecium nicotinate nucleotide adenylyltransferase for X-ray crystallography and biophysical studies

Nicotinate nucleotide adenylyltransferase (NNAT) has been a significant research focus on druggable targets, given its indispensability in the biosynthesis of NAD+, which is crucial to the survival of bacterial pathogens. However, no information is available on the structure-function of Enterococcus faecium NNAT (EfNNAT). This study established the expression and purification protocol for obtaining a high-yield recombinant EfNNAT using the E. coli expression system and a single-step IMAC purification method. Approximately 101 mg of EfNNAT was obtained per 7.8 g of wet E. coli cells, estimated to be over 98 % pure. We further characterized the biophysical structure and determined the three-dimensional structure of the EfNNAT. Biophysical studies revealed a dimeric protein with a higher α-helical composition. The highly stable protein crystalizes in multiple conditions, yielding high-quality crystals diffracting between 1.78 and 2.80 Å. Two high-resolution crystal structures of EfNNAT in its native and adenine-bound forms were determined at 1.90 Å and 1.82 Å, respectively. The X-ray structures of the EfNNAT revealed the presence of phosphate and sulfate ions occupying and interacting with conserved amino acid residues within the putative substrate binding site, hence providing insight into the probable substrate preference of EfNNAT and, consequently, why EfNNAT may not prefer β-nicotinamide mononucleotide as a substrate. With the accessibility to high-resolution structures of EfNNAT, further structural evaluation and drug-based screening can be achieved. Hence, we anticipate that this study will provide the basis for the discovery of structure-based inhibitors against this enzyme.

Protein-DNA Binding Residues Prediction Using a Deep Learning Model With Hierarchical Feature Extraction

Biologically important effects occur when proteins bind to other substances, of which binding to DNA is a crucial one. Therefore, accurate identification of protein-DNA binding residues is important for further understanding of the protein-DNA interaction mechanism. Although wet-lab methods can accurately obtain the location of bound residues, it requires significant human, financial and time costs. There is thus an urgent need to develop efficient computational-based methods. Most current state-of-the-art methods are two-step approaches: the first step uses a sliding window technique to extract residue features; the second step uses each residue as an input to the model for prediction. This has a negative impact on the efficiency of prediction and ease of use. In this study, we propose a sequence-to-sequence (seq2seq) model that can input the entire protein sequence of variable length and use two modules, Transformer Encoder Block and Feature Extracting Block, for hierarchical feature extraction, where Transformer Encoder Block is used to extract global features, and then Feature Extracting Block is used to extract local features to further improve the recognition capability of the model. The comparison results on two benchmark datasets, namely PDNA-543 and PDNA-41, prove the effectiveness of our method in identifying protein-DNA binding residues.

Seedability: optimizing alignment parameters for sensitive sequence comparison

Motivation

Most sequence alignment techniques make use of exact k-mer hits, called seeds, as anchors to optimize alignment speed. A large number of bioinformatics tools employing seed-based alignment techniques, such as Minimap2 , use a single value of k per sequencing technology, without a strong guarantee that this is the best possible value. Given the ubiquity of sequence alignment, identifying values of k that lead to more sensitive alignments is thus an important task. To aid this, we present Seedability , a seed-based alignment framework designed for estimating an optimal seed k-mer length (as well as a minimal number of shared seeds) based on a given alignment identity threshold. In particular, we were motivated to make Minimap2 more sensitive in the pairwise alignment of short sequences.

Results

The experimental results herein show improved alignments of short and divergent sequences when using the parameter values determined by Seedability in comparison to the default values of Minimap2 . We also show several cases of pairs of real divergent sequences, where the default parameter values of Minimap2 yield no output alignments, but the values output by Seedability produce plausible alignments.

Availability and implementation

https://github.com/lorrainea/Seedability (distributed under GPL v3.0).

Localization of the Catalytic Domain of Copepod Luciferases: Analysis of Truncated Mutants of the Metridia longa Luciferase

Luciferases from copepods Metridia longa and Gaussia princeps are successfully used as bioluminescent reporters for in vivo and in vitro assays. Here, we report the minimal sequence of copepod luciferases required for bioluminescence activity that was revealed by gradual deletions of sequence encoding the smallest MLuc7 isoform of M. longa luciferase. The single catalytic domain is shown to reside within the G32-A149 MLuc7 sequence and to be formed by both non-identical repeats, including 10 conserved Cys residues. Because this part of MLuc7 displays high homology with those of other copepod luciferases, our suggestion is that the determined boundaries of the catalytic domain are the same for all known copepod luciferases. The involvement of the flexible C-terminus in the retention of the bioluminescent reaction product in the substrate-binding cavity was confirmed by structural modeling and kinetics study. We also demonstrate that the ML7-N10 mutant (15.4 kDa) with deletion of ten amino acid residues at the N-terminus can be successfully used as a miniature bioluminescent reporter in living cells. Application of a shortened reporter may surely reduce the metabolic load on the host cells and decrease steric and functional interference at its use as a part of hybrid proteins.

Screening and identification of cyprinid herpesvirus 2 (CyHV-2) ORF55-interacting proteins by phage display

BACKGROUND

Cyprinid herpesvirus 2 (CyHV-2) is a pathogenic fish virus belonging to family Alloherpesviridae. The CyHV-2 gene encoding thymidine kinase (TK) is an important virulence-associated factor. Therefore, we aimed to investigate the biological function of open reading frame 55 (ORF55) in viral replication.

METHODS

Purified CyHV-2 ORF55 protein was obtained by prokaryotic expression, and the interacting peptide was screened out using phage display. Host interacting proteins were then predicted and validated.

RESULTS

ORF55 was efficiently expressed in the prokaryotic expression system. Protein and peptide interaction prediction and dot-blot overlay assay confirmed that peptides identified by phage display could interact with the ORF55 protein. Comparing the peptides to the National Center for Biotechnology Information database revealed four potential interacting proteins. Reverse transcription quantitative PCR results demonstrated high expression of an actin-binding Rho-activating protein in the latter stages of virus-infected cells, and molecular docking, cell transfection and coimmunoprecipitation experiments confirmed that it interacted with the ORF55 protein.

CONCLUSION

During viral infection, the ORF55 protein exerts its biological function through interactions with host proteins. The specific mechanisms remain to be further explored.

Novel phytochemical inhibitors targeting monkeypox virus thymidine and serine/threonine kinase: integrating computational modeling and molecular dynamics simulation

Due to the rapid spread of the monkeypox virus and rise in the number of cases, there is an urgent need for the development of effective drugs against the infection. Serine/threonine protein kinase (Ser/Thr kinase) and Thymidine Kinase (TK) plays an imperative role in the replication and virulence of monkeypox virus and thus is deliberated as an attractive target in anti-viral drug development. In the present study, the 3D structure of monkeypox virus Ser/Thr kinase and TK was generated via molecular modeling techniques and performed their thorough structural analysis. We have screened potent anti-viral phytochemicals from the literature to inhibit Ser/Thr kinase and TK. As part of the initial screening, the physicochemical properties of the compounds were examined. Following this, a structure-based molecular docking technique was used to select compounds based on their binding affinity towards Ser/Thr kinase and TK. In order to find more potent hits against Ser/Thr kinase and TK, further examinations of ADMET properties, PAINS patterns and blood-brain barrier permeability were conducted. As a result, thalimonine and galanthamine were identified from the screening process bearing appreciable binding affinity towards Ser/Thr kinase and TK respectively, which showed a worthy set of drug-like properties. In the end, molecular dynamics simulations were performed for 100 ns, which showed decent stability of both protein-ligand complex throughout the trajectory. Due to the possibility that both monkeypox virus target proteins may be inhibited by thalimonine and galanthamine, our study highlights the need to investigate in vivo effects of thalimonine and galanthamine.Communicated by Ramaswamy H. Sarma.

The molecular mechanism of sialic acid transport mediated by Sialin

Malfunction of the sialic acid transporter caused by various genetic mutations in the SLC17A5 gene encoding Sialin leads to a spectrum of neurodegenerative conditions called free sialic acid storage disorders. Unfortunately, how Sialin transports sialic acid/proton (H+) and how pathogenic mutations impair its function are poorly defined. Here, we present the structure of human Sialin in an inward-facing partially open conformation determined by cryo-electron microscopy, representing the first high-resolution structure of any human SLC17 member. Our analysis reveals two unique features in Sialin: (i) The H+ coupling/sensing requires two highly conserved Glu residues (E171 and E175) instead of one (E175) as implied in previous studies; and (ii) the normal function of Sialin requires the stabilization of a cytosolic helix, which has not been noticed in the literature. By mapping known pathogenic mutations, we provide mechanistic explanations for corresponding functional defects. We propose a structure-based mechanism for sialic acid transport mediated by Sialin.

The effect of the genomic GC content bias of prokaryotic organisms on the secondary structures of their proteins

One of the main characteristics of prokaryotic genomes is the ratio in which guanine-cytosine bases are used in their DNA sequences. This is known as the genomic GC content and varies widely, from values below 20% to values greater than 74%. It has been demonstrated that the genomic GC content varies in accordance with the phylogenetic distribution of organisms and influences the amino acid composition of their corresponding proteomes. This bias is particularly important for amino acids that are coded by GC content-rich codons such as alanine, glycine, and proline, as well as amino acids that are coded by AT-rich codons, such as lysine, asparagine, and isoleucine. In our study, we extend these results by considering the effect of the genomic GC content on the secondary structure of proteins. On a set of 192 representative prokaryotic genomes and proteome sequences, we identified through a bioinformatic study that the composition of the secondary structures of the proteomes varies in relation to the genomic GC content; random coils increase as the genomic GC content increases, while alpha-helices and beta-sheets present an inverse relationship. In addition, we found that the tendency of an amino acid to form part of a secondary structure of proteins is not ubiquitous, as previously expected, but varies according to the genomic GC content. Finally, we discovered that for some specific groups of orthologous proteins, the GC content of genes biases the composition of secondary structures of the proteins for which they code.

Contact-Assisted Threading in Low-Homology Protein Modeling

The ability to successfully predict the three-dimensional structure of a protein from its amino acid sequence has made considerable progress in the recent past. The progress is propelled by the improved accuracy of deep learning-based inter-residue contact map predictors coupled with the rising growth of protein sequence databases. Contact map encodes interatomic interaction information that can be exploited for highly accurate prediction of protein structures via contact map threading even for the query proteins that are not amenable to direct homology modeling. As such, contact-assisted threading has garnered considerable research effort. In this chapter, we provide an overview of existing contact-assisted threading methods while highlighting the recent advances and discussing some of the current limitations and future prospects in the application of contact-assisted threading for improving the accuracy of low-homology protein modeling.

Bombyx mori Cecropin D could trigger cancer cell apoptosis by interacting with mitochondrial cardiolipin

Cecropin D is an antimicrobial peptide from Bombyx mori displaying anticancer and pro-apoptotic activities and, together with Cecropin XJ and Cecropin A, one of the very few peptides targeting esophageal cancer. Cecropin D displays poor similarity to other cecropins but a remarkable similarity in the structure and activity spectrum with Cecropin A and Cecropin XJ, offering the possibility to highlight key motifs at the base of the biological activity. In this work we show by NMR and MD simulations that Cecropin D is partially structured in solution and stabilizes its two-helix folding upon interaction with biomimetic membranes. Simulations show that Cecropin D strongly interacts with the surface of cancer cell biomimetic bilayers where it recognises the phosphatidylserine headgroup often exposed in the outer leaflet of cancerous cells by means of specific salt bridges. Cecropin D is also able to penetrate deeply in bilayers containing cardiolipin, a phospholipid found in mitochondria, causing significant destabilization in the lipid packing which might account for its pro-apoptotic activity. In bacterial membranes, phosphatidylglycerol and phosphatidylethanolamine act synergically by electrostatically attracting cecropin D and providing access to the membrane core, respectively.

A combined in silico and in vivo approach to the structure-function annotation of SPD-2 provides mechanistic insight into its functional diversity

Centrosomes are organelles that function as hubs of microtubule nucleation and organization, with key roles in organelle positioning, asymmetric cell division, ciliogenesis, and signaling. Aberrant centrosome number, structure or function is linked to neurodegenerative diseases, developmental abnormalities, ciliopathies, and tumor development. A major regulator of centrosome biogenesis and function in C. elegans is the conserved Spindle-defective protein 2 (SPD-2), a homolog of the human CEP-192 protein. CeSPD-2 is required for centrosome maturation, centriole duplication, spindle assembly and possibly cell polarity establishment. Despite its importance, the specific molecular mechanism of CeSPD-2 regulation and function is poorly understood. Here, we combined computational analysis with cell biology approaches to uncover possible structure-function relationships of CeSPD-2 that may shed mechanistic light on its function. Domain prediction analysis corroborated and refined previously identified coiled-coils and ASH (Aspm-SPD-2 Hydin) domains and identified new domains: a GEF domain, an Ig-like domain, and a PDZ-like domain. In addition to these predicted structural features, CeSPD-2 is also predicted to be intrinsically disordered. Surface electrostatic maps identified a large basic region unique to the ASH domain of CeSPD-2. This basic region overlaps with most of the residues predicted to be involved in protein-protein interactions. In vivo, ASH::GFP localized to centrosomes and centrosome-associated microtubules. Our analysis groups ASH domains, PapD, Usher chaperone domains, and Major Sperm Protein (MSP) domains into a single superfold within the larger Immunoglobulin superfamily. This study lays the groundwork for designing rational hypothesis-based experiments to uncover the mechanisms of CeSPD-2 function in vivo.Abbreviations: AIR, Aurora kinase; ASH, Aspm-SPD-2 Hydin; ASP, Abnormal Spindle Protein; ASPM, Abnormal Spindle-like Microcephaly-associated Protein; CC, coiled-coil; CDK, Cyclin-dependent Kinase; Ce, Caenorhabditis elegans; CEP, Centrosomal Protein; CPAP, centrosomal P4.1-associated protein; D, Drosophila; GAP, GTPase activating protein; GEF, GTPase guanine nucleotide exchange factor; Hs, Homo sapiens/Human; Ig, Immunoglobulin; MAP, Microtubule associated Protein; MSP, Major Sperm Protein; MDP, Major Sperm Domain-Containing Protein; OCRL-1, Golgi endocytic trafficking protein Inositol polyphosphate 5-phosphatase; PAR, abnormal embryonic PARtitioning of the cytosol; PCM, Pericentriolar material; PCMD, pericentriolar matrix deficient; PDZ, PSD95/Dlg-1/zo-1; PLK, Polo like kinase; RMSD, Root Mean Square Deviation; SAS, Spindle assembly abnormal proteins; SPD, Spindle-defective protein; TRAPP, TRAnsport Protein Particle; Xe, Xenopus; ZYG, zygote defective protein.

New feature extraction from phylogenetic profiles improved the performance of pathogen-host interactions

Motivation

The understanding of pathogen-host interactions (PHIs) is essential and challenging research because this potentially provides the mechanism of molecular interactions between different organisms. The experimental exploration of PHI is time-consuming and labor-intensive, and computational approaches are playing a crucial role in discovering new unknown PHIs between different organisms. Although it has been proposed that most machine learning (ML)–based methods predict PHI, these methods are all based on the structure-based information extracted from the sequence for prediction. The selection of feature values is critical to improving the performance of predicting PHI using ML.

Results

This work proposed a new method to extract features from phylogenetic profiles as evolutionary information for predicting PHI. The performance of our approach is better than that of structure-based and ML-based PHI prediction methods. The five different extract models proposed by our approach combined with structure-based information significantly improved the performance of PHI, suggesting that combining phylogenetic profile features and structure-based methods could be applied to the exploration of PHI and discover new unknown biological relativity.

Availability and implementation

The KPP method is implemented in the Java language and is available at https://github.com/yangfangs/KPP.

CRFalign: A Sequence-Structure Alignment of Proteins Based on a Combination of HMM-HMM Comparison and Conditional Random Fields

Sequence-structure alignment for protein sequences is an important task for the template-based modeling of 3D structures of proteins. Building a reliable sequence-structure alignment is a challenging problem, especially for remote homologue target proteins. We built a method of sequence-structure alignment called CRFalign, which improves upon a base alignment model based on HMM-HMM comparison by employing pairwise conditional random fields in combination with nonlinear scoring functions of structural and sequence features. Nonlinear scoring part is implemented by a set of gradient boosted regression trees. In addition to sequence profile features, various position-dependent structural features are employed including secondary structures and solvent accessibilities. Training is performed on reference alignments at superfamily levels or twilight zone chosen from the SABmark benchmark set. We found that CRFalign method produces relative improvement in terms of average alignment accuracies for validation sets of SABmark benchmark. We also tested CRFalign on 51 sequence-structure pairs involving 15 FM target domains of CASP14, where we could see that CRFalign leads to an improvement in average modeling accuracies in these hard targets (TM-CRFalign ≃42.94%) compared with that of HHalign (TM-HHalign ≃39.05%) and also that of MRFalign (TM-MRFalign ≃36.93%). CRFalign was incorporated to our template search framework called CRFpred and was tested for a random target set of 300 target proteins consisting of Easy, Medium and Hard sets which showed a reasonable template search performance.

Structural Insights into the Intrinsically Disordered GPCR C-Terminal Region, Major Actor in Arrestin-GPCR Interaction

Arrestin-dependent pathways are a central component of G protein-coupled receptor (GPCRs) signaling. However, the molecular processes regulating arrestin binding are to be further illuminated, in particular with regard to the structural impact of GPCR C-terminal disordered regions. Here, we used an integrated biophysical strategy to describe the basal conformations of the C-terminal domains of three class A GPCRs, the vasopressin V2 receptor (V2R), the growth hormone secretagogue or ghrelin receptor type 1a (GHSR) and the β2-adernergic receptor (β2AR). By doing so, we revealed the presence of transient secondary structures in these regions that are potentially involved in the interaction with arrestin. These secondary structure elements differ from those described in the literature in interaction with arrestin. This suggests a mechanism where the secondary structure conformational preferences in the C-terminal regions of GPCRs could be a central feature for optimizing arrestins recognition.

Association of the receptor for activated C-kinase 1 with ribosomes in Plasmodium falciparum

The receptor for activated C-kinase 1 (RACK1), a highly conserved eukaryotic protein, is known to have many varying biological roles and functions. Previous work has established RACK1 as a ribosomal protein, with defined regions important for ribosome binding in eukaryotic cells. In Plasmodium falciparum, RACK1 has been shown to be required for parasite growth, however, conflicting evidence has been presented about RACK1 ribosome binding and its role in mRNA translation. Given the importance of RACK1 as a regulatory component of mRNA translation and ribosome quality control, the case could be made in parasites that RACK1 either binds or does not bind the ribosome. Here, we used bioinformatics and transcription analyses to further characterize the P. falciparum RACK1 protein. Based on homology modeling and structural analyses, we generated a model of P. falciparum RACK1. We then explored mutant and chimeric human and P. falciparum RACK1 protein binding properties to the human and P. falciparum ribosome. We found that WT, chimeric, and mutant RACK1 exhibit distinct ribosome interactions suggesting different binding characteristics for P. falciparum and human RACK1 proteins. The ribosomal binding of RACK1 variants in human and parasite cells shown here demonstrates that although RACK1 proteins have highly conserved sequences and structures across species, ribosomal binding is affected by species-specific alterations to this protein. In conclusion, we show that in the case of P. falciparum, contrary to the structural data, RACK1 is found to bind ribosomes and actively translating polysomes in parasite cells.

Understanding Diversity, Evolution, and Structure of Small Heat Shock Proteins in Annelida Through in Silico Analyses

Small heat shock proteins (sHsps) are oligomeric stress proteins characterized by an α-crystallin domain (ACD). These proteins are localized in different subcellular compartments and play critical roles in the stress physiology of tissues, organs, and whole multicellular eukaryotes. They are ubiquitous proteins found in all living organisms, from bacteria to mammals, but they have never been studied in annelids. Here, a data set of 23 species spanning the annelid tree of life, including mostly transcriptomes but also two genomes, was interrogated and 228 novel putative sHsps were identified and manually curated. The analysis revealed very high protein diversity and showed that a significant number of sHsps have a particular dimeric architecture consisting of two tandemly repeated ACDs. The phylogenetic analysis distinguished three main clusters, two of them containing both monomeric sHsps, and ACDs located downstream in the dimeric sHsps, and the other one comprising the upstream ACDs from those dimeric forms. Our results support an evolutionary history of these proteins based on duplication events prior to the Spiralia split. Monomeric sHsps 76) were further divided into five subclusters. Physicochemical properties, subcellular location predictions, and sequence conservation analyses provided insights into the differentiating elements of these putative functional groups. Strikingly, three of those subclusters included sHsps with features typical of metazoans, while the other two presented characteristics resembling non-metazoan proteins. This study provides a solid background for further research on the diversity, evolution, and function in the family of the sHsps. The characterized annelid sHsps are disclosed as essential for improving our understanding of this important family of proteins and their pleotropic functions. The features and the great diversity of annelid sHsps position them as potential powerful molecular biomarkers of environmental stress for acting as prognostic tool in a diverse range of environments.

3DLigandSite: structure-based prediction of protein-ligand binding sites

3DLigandSite is a web tool for the prediction of ligand-binding sites in proteins. Here, we report a significant update since the first release of 3DLigandSite in 2010. The overall methodology remains the same, with candidate binding sites in proteins inferred using known binding sites in related protein structures as templates. However, the initial structural modelling step now uses the newly available structures from the AlphaFold database or alternatively Phyre2 when AlphaFold structures are not available. Further, a sequence-based search using HHSearch has been introduced to identify template structures with bound ligands that are used to infer the ligand-binding residues in the query protein. Finally, we introduced a machine learning element as the final prediction step, which improves the accuracy of predictions and provides a confidence score for each residue predicted to be part of a binding site. Validation of 3DLigandSite on a set of 6416 binding sites obtained 92% recall at 75% precision for non-metal binding sites and 52% recall at 75% precision for metal binding sites. 3DLigandSite is available at https://www.wass-michaelislab.org/3dligandsite. Users submit either a protein sequence or structure. Results are displayed in multiple formats including an interactive Mol* molecular visualization of the protein and the predicted binding sites.

Building Biological Relevance Into Integrative Modelling of Macromolecular Assemblies

Recent advances in structural biophysics and integrative modelling methods now allow us to decipher the structures of large macromolecular assemblies. Understanding the dynamics and mechanisms involved in their biological function requires rigorous integration of all available data. We have developed a complete modelling pipeline that includes analyses to extract biologically significant information by consistently combining automated and interactive human-guided steps. We illustrate this idea with two examples. First, we describe the ryanodine receptor, an ion channel that controls ion flux across the cell membrane through transitions between open and closed states. The conformational changes associated with the transitions are small compared to the considerable system size of the receptor; it is challenging to consistently track these states with the available cryo-EM structures. The second example involves homologous recombination, in which long filaments of a recombinase protein and DNA catalyse the exchange of homologous DNA strands to reliably repair DNA double-strand breaks. The nucleoprotein filament reaction intermediates in this process are short-lived and heterogeneous, making their structures particularly elusive. The pipeline we describe, which incorporates experimental and theoretical knowledge combined with state-of-the-art interactive and immersive modelling tools, can help overcome these challenges. In both examples, we point to new insights into biological processes that arise from such interdisciplinary approaches.

Progressive assembly of multi-domain protein structures from cryo-EM density maps

Progress in cryo-electron microscopy has provided the potential for large-size protein structure determination. However, the success rate for solving multi-domain proteins remains low because of the difficulty in modelling inter-domain orientations. Here we developed domain enhanced modeling using cryo-electron microscopy (DEMO-EM), an automatic method to assemble multi-domain structures from cryo-electron microscopy maps through a progressive structural refinement procedure combining rigid-body domain fitting and flexible assembly simulations with deep-neural-network inter-domain distance profiles. The method was tested on a large-scale benchmark set of proteins containing up to 12 continuous and discontinuous domains with medium- to low-resolution density maps, where DEMO-EM produced models with correct inter-domain orientations (template modeling score (TM-score) >0.5) for 97% of cases and outperformed state-of-the-art methods. DEMO-EM was applied to the severe acute respiratory syndrome coronavirus 2 genome and generated models with average TM-score and root-mean-square deviation of 0.97 and 1.3 Å, respectively, with respect to the deposited structures. These results demonstrate an efficient pipeline that enables automated and reliable large-scale multi-domain protein structure modelling from cryo-electron microscopy maps.

Molecular modelling studies and identification of novel phytochemical inhibitor of DLL3

Prostate cancer has been recently considered the most diagnosed cancer in male. DLL3 is overexpressed in CRPC-NE but not in localised prostate cancer or BPH. There are no effective treatments for neuroendocrine differentiated prostate cancer due to a lack of understanding of DLL3 structure and function. The structure of DLL3 is not yet determined using any experimental techniques. Hence, the structure-based drug discovery approach against prostate cancer has not shown great success. In present study, molecular modelling techniques were employed to generate three-dimensional structure of DLL3 and performed its thorough structural analysis. Further, all-atom molecular dynamics simulation was performed to obtain energetically favourable conformation. Further, we used a virtual screening using a library of >13800 phytochemicals from the IMPPAT database and other literature to select the best possible phytochemical inhibitor for DLL3 and identified the top five compounds. Relative binding affinity was calculated using the MM-PBSA approach. ADMET properties of the screened compounds reveal the toxic effect of Gnemonol C. We believe these studied physicochemical properties, functional domain identification, and binding site identification would be very useful to gain more structural and functional insights of DLL3; also, it can be used to infer their pharmacodynamics properties of DLL3 which was recently reported as an important prostate cancer target. The current study also proposes that Ergosterol Peroxide, Dioslupecin A, Mulberrofuran K, and Caracurine V have strong affinities and could serve as plausible inhibitors against DLL3. We believe this study would further help develop better drug candidates against neuroendocrine prostate cancer.Communicated by Ramaswamy H. Sarma.

A sequence space search engine for computational protein design to modulate molecular functionality

De-novo protein design explores the untapped sequence space that is otherwise less discovered during the evolutionary process. This necessitates an efficient sequence space search engine for effective convergence in computational protein design. We propose a greedy simulated annealing-based Monte-Carlo parallel search algorithm for better sequence-structure compatibility probing in protein design. The guidance provided by the evolutionary profile, the greedy approach, and the cooling schedule adopted in the Monte Carlo simulation ensures sufficient exploration and exploitation of the search space leading to faster convergence. On evaluating the proposed algorithm, we find that a dataset of 76 target scaffolds report an average root-mean-square-deviation (RMSD) of 1.07 Å and an average TM-Score of 0.93 with the modeled designed protein sequences. High sequence recapitulation of 48.7% (59.4%) observed in the design sequences for all (hydrophobic) solvent-inaccessible residues again establish the goodness of the proposed algorithm. A high (93.4%) intra-group recapitulation of hydrophobic residues in the solvent-inaccessible region indicates that the proposed protein design algorithm preserves the core residues in the protein and provides alternative residue combinations in the solvent-accessible regions of the target protein. Furthermore, a COFACTOR-based protein functional analysis shows that the design sequences exhibit altered molecular functionality and introduce new molecular functions compared to the target scaffolds.Communicated by Ramaswamy H. Sarma.

Enhancing protein inter-residue real distance prediction by scrutinising deep learning models

Protein structure prediction (PSP) has achieved significant progress lately via prediction of inter-residue distances using deep learning models and exploitation of the predictions during conformational search. In this context, prediction of large inter-residue distances and also prediction of distances between residues separated largely in the protein sequence remain challenging. To deal with these challenges, state-of-the-art inter-residue distance prediction algorithms have used large sets of coevolutionary and non-coevolutionary features. In this paper, we argue that the more the types of features used, the more the kinds of noises introduced and then the deep learning model has to overcome the noises to improve the accuracy of the predictions. Also, multiple features capturing similar underlying characteristics might not necessarily have significantly better cumulative effect. So we scrutinise the feature space to reduce the types of features to be used, but at the same time, we strive to improve the prediction accuracy. Consequently, for inter-residue real distance prediction, in this paper, we propose a deep learning model named scrutinised distance predictor (SDP), which uses only 2 coevolutionary and 3 non-coevolutionary features. On several sets of benchmark proteins, our proposed SDP method improves mean Local Distance Different Test (LDDT) scores at least by 10% over existing state-of-the-art methods. The SDP program along with its data is available from the website https://gitlab.com/mahnewton/sdp .

Cryo-EM reveals unique structural features of the FhuCDB Escherichia coli ferrichrome importer

As one of the most elegant biological processes developed in bacteria, the siderophore-mediated iron uptake demands the action of specific ATP-binding cassette (ABC) importers. Although extensive studies have been done on various ABC importers, the molecular basis of these iron-chelated-siderophore importers are still not fully understood. Here, we report the structure of a ferrichrome importer FhuCDB from Escherichia coli at 3.4 Å resolution determined by cryo electron microscopy. The structure revealed a monomeric membrane subunit of FhuB with a substrate translocation pathway in the middle. In the pathway, there were unique arrangements of residues, especially layers of methionines. Important residues found in the structure were interrogated by mutagenesis and functional studies. Surprisingly, the importer’s ATPase activity was decreased upon FhuD binding, which deviated from the current understanding about bacterial ABC importers. In summary, to the best of our knowledge, these studies not only reveal a new structural twist in the type II ABC importer subfamily, but also provide biological insights in the transport of iron-chelated siderophores.

Wenxin Hu et al. use cryo-EM and biochemical assays to describe the functional activity and structure of the ferrichrome importer, FhuCDB in E. coli. Their results provide further insight on the mechanism of siderophore transport in bacteria.

Cryo-EM structure of a microtubule-bound parasite kinesin motor and implications for its mechanism and inhibition

Plasmodium parasites cause malaria and are responsible annually for hundreds of thousands of deaths. Kinesins are a superfamily of microtubule-dependent ATPases that play important roles in the parasite replicative machinery, which is a potential target for antiparasite drugs. Kinesin-5, a molecular motor that cross-links microtubules, is an established antimitotic target in other disease contexts, but its mechanism in Plasmodium falciparum is unclear. Here, we characterized P. falciparum kinesin-5 (PfK5) using cryo-EM to determine the motor's nucleotide-dependent microtubule-bound structure and introduced 3D classification of individual motors into our microtubule image processing pipeline to maximize our structural insights. Despite sequence divergence in PfK5, the motor exhibits classical kinesin mechanochemistry, including ATP-induced subdomain rearrangement and cover neck bundle formation, consistent with its plus-ended directed motility. We also observed that an insertion in loop5 of the PfK5 motor domain creates a different environment in the well-characterized human kinesin-5 drug-binding site. Our data reveal the possibility for selective inhibition of PfK5 and can be used to inform future exploration of Plasmodium kinesins as antiparasite targets.

HSymM-guided engineering of the immunodominant p53 transactivation domain putative peptide antigen for improved binding to its anti-p53 monoclonal antibody

A novel engineering strategy to improve autoantibody detection with peptide fragments derived from the parent antigen is presented. The model system studied was the binding of the putative p53 TAD peptide antigen (residues 46-55) to its cognate anti-p53 antibody, ab28. Each engineered peptide contained the full decapeptide epitope and differed only in the flanking regions. Since minimal structural information was available to guide the design, a simple epitope:paratope binding model was applied. The Hidden Symmetry Model, which we recently reported, was used to guide peptide design and estimate per-residue contributions to interaction free energy as a function of added C- and N-terminal flanking peptides. Twenty-four peptide constructs were designed, synthesized, and assessed for binding affinity to ab28 by surface plasmon resonance, and a subset of these peptides were evaluated in a simulated immunoassay for limit of detection. Many peptides exhibited over 200-fold enhancements in binding affinity and improved limits of detection. The epitope was reevaluated and is proposed to be the undecapeptide corresponding to residues 45-55. HSymM calculated binding free energy and experimental data were found to be in good agreement (R2 > 0.75).

Proteomic Tools for the Analysis of Cytoskeleton Proteins

Proteomic analyses have become an essential part of the toolkit of the molecular biologist, given the widespread availability of genomic data and open source or freely accessible bioinformatics software. Tools are available for detecting homologous sequences, recognizing functional domains, and modeling the three-dimensional structure for any given protein sequence, as well as for predicting interactions with other proteins or macromolecules. Although a wealth of structural and functional information is available for many cytoskeletal proteins, with representatives spanning all of the major subfamilies, the majority of cytoskeletal proteins remain partially or totally uncharacterized. Moreover, bioinformatics tools provide a means for studying the effects of synthetic mutations or naturally occurring variants of these cytoskeletal proteins. This chapter discusses various freely available proteomic analysis tools, with a focus on in silico prediction of protein structure and function. The selected tools are notable for providing an easily accessible interface for the novice while retaining advanced functionality for more experienced computational biologists.

The Structure Biology of Tau and Clue for Aggregation Inhibitor Design

Tau is a microtubule-associated protein that is mainly expressed in central and peripheral nerve systems. Tau binds to tubulin and regulates assembly and stabilization of microtubule, thus playing a critical role in neuron morphology, axon development and navigation. Tau is highly stable under normal conditions; however, there are several factors that can induce or promote aggregation of tau, forming neurofibrillary tangles. Neurofibrillary tangles are toxic to neurons, which may be related to a series of neurodegenerative diseases including Alzheimer's disease. Thus, tau is widely accepted as an important therapeutic target for neurodegenerative diseases. While the monomeric structure of tau is highly disordered, the aggregate structure of tau is formed by closed packing of β-stands. Studies on the structure of tau and the structural transition mechanism provide valuable information on the occurrence, development, and therapy of tauopathies. In this review, we summarize recent progress on the structural investigation of tau and based on which we discuss aggregation inhibitor design.

Improving fragment-based ab initio protein structure assembly using low-accuracy contact-map predictions

Sequence-based contact prediction has shown considerable promise in assisting non-homologous structure modeling, but it often requires many homologous sequences and a sufficient number of correct contacts to achieve correct folds. Here, we developed a method, C-QUARK, that integrates multiple deep-learning and coevolution-based contact-maps to guide the replica-exchange Monte Carlo fragment assembly simulations. The method was tested on 247 non-redundant proteins, where C-QUARK could fold 75% of the cases with TM-scores (template-modeling scores) ≥0.5, which was 2.6 times more than that achieved by QUARK. For the 59 cases that had either low contact accuracy or few homologous sequences, C-QUARK correctly folded 6 times more proteins than other contact-based folding methods. C-QUARK was also tested on 64 free-modeling targets from the 13th CASP (critical assessment of protein structure prediction) experiment and had an average GDT_TS (global distance test) score that was 5% higher than the best CASP predictors. These data demonstrate, in a robust manner, the progress in modeling non-homologous protein structures using low-accuracy and sparse contact-map predictions.

Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations

Structure prediction for proteins lacking homologous templates in the Protein Data Bank (PDB) remains a significant unsolved problem. We developed a protocol, C-I-TASSER, to integrate interresidue contact maps from deep neural-network learning with the cutting-edge I-TASSER fragment assembly simulations. Large-scale benchmark tests showed that C-I-TASSER can fold more than twice the number of non-homologous proteins than the I-TASSER, which does not use contacts. When applied to a folding experiment on 8,266 unsolved Pfam families, C-I-TASSER successfully folded 4,162 domain families, including 504 folds that are not found in the PDB. Furthermore, it created correct folds for 85% of proteins in the SARS-CoV-2 genome, despite the quick mutation rate of the virus and sparse sequence profiles. The results demonstrated the critical importance of coupling whole-genome and metagenome-based evolutionary information with optimal structure assembly simulations for solving the problem of non-homologous protein structure prediction.

PvP01-DB: computational structural and functional characterization of soluble proteome of PvP01 strain of Plasmodium vivax

Despite Plasmodium vivax being the main offender in the majority of malarial infections, very little information is available about its adaptation and development in humans. Its capability for activating relapsing infections through its dormant liver stage and resistance to antimalarial drugs makes it as one of the major challenges in eradicating malaria. Noting the immediate necessity for the availability of a comprehensive and reliable structural and functional repository for P. vivax proteome, here we developed a web resource for the new reference genome, PvP01, furnishing information on sequence, structure, functions, active sites and metabolic pathways compiled and predicted using some of the state-of-the-art methods in respective fields. The PvP01 web resource comprises organized data on the soluble proteome consisting of 3664 proteins in blood and liver stages of malarial cycle. The current public resources represent only 163 proteins of soluble proteome of PvP01, with complete information about their molecular function, biological process and cellular components. Also, only 46 proteins of P. vivax have experimentally determined structures. In this milieu of extreme scarcity of structural and functional information, PvP01 web resource offers meticulously validated structures of 3664 soluble proteins. The sequence and structure-based functional characterization led to a quantum leap from 163 proteins available presently to whole soluble proteome offered through PvP01 web resource. We believe PvP01 web resource will serve the researchers in identifying novel protein drug targets and in accelerating the development of structure-based new drug candidates to combat malaria.

Database Availability: http://www.scfbio-iitd.res.in/PvP01

O-GlcNAcylation Prediction: An Unattained Objective

BACKGROUND

O-GlcNAcylation is an essential post-translational modification (PTM) in mammalian cells. It consists in the addition of a N-acetylglucosamine (GlcNAc) residue onto serines or threonines by an O-GlcNAc transferase (OGT). Inhibition of OGT is lethal, and misregulation of this PTM can lead to diverse pathologies including diabetes, Alzheimer's disease and cancers. Knowing the location of O-GlcNAcylation sites and the ability to accurately predict them is therefore of prime importance to a better understanding of this process and its related pathologies.

PURPOSE

Here, we present an evaluation of the current predictors of O-GlcNAcylation sites based on a newly built dataset and an investigation to improve predictions.

METHODS

Several datasets of experimentally proven O-GlcNAcylated sites were combined, and the resulting meta-dataset was used to evaluate three prediction tools. We further defined a set of new features following the analysis of the primary to tertiary structures of experimentally proven O-GlcNAcylated sites in order to improve predictions by the use of different types of machine learning techniques.

RESULTS

Our results show the failure of currently available algorithms to predict O-GlcNAcylated sites with a precision exceeding 9%. Our efforts to improve the precision with new features using machine learning techniques do succeed for equal proportions of O-GlcNAcylated and non-O-GlcNAcylated sites but fail like the other tools for real-life proportions where ~1.4% of S/T are O-GlcNAcylated.

CONCLUSION

Present-day algorithms for O-GlcNAcylation prediction narrowly outperform random prediction. The inclusion of additional features, in combination with machine learning algorithms, does not enhance these predictions, emphasizing a pressing need for further development. We hypothesize that the improvement of prediction algorithms requires characterization of OGT's partners.

Predicting phosphorylation sites using machine learning by integrating the sequence, structure, and functional information of proteins

BACKGROUND

Post-translational modification (PTM) is a biological process that alters proteins and is therefore involved in the regulation of various cellular activities and pathogenesis. Protein phosphorylation is an essential process and one of the most-studied PTMs: it occurs when a phosphate group is added to serine (Ser, S), threonine (Thr, T), or tyrosine (Tyr, Y) residue. Dysregulation of protein phosphorylation can lead to various diseases-most commonly neurological disorders, Alzheimer's disease, and Parkinson's disease-thus necessitating the prediction of S/T/Y residues that can be phosphorylated in an uncharacterized amino acid sequence. Despite a surplus of sequencing data, current experimental methods of PTM prediction are time-consuming, costly, and error-prone, so a number of computational methods have been proposed to replace them. However, phosphorylation prediction remains limited, owing to substrate specificity, performance, and the diversity of its features.

METHODS

In the present study we propose machine-learning-based predictors that use the physicochemical, sequence, structural, and functional information of proteins to classify S/T/Y phosphorylation sites. Rigorous feature selection, the minimum redundancy/maximum relevance approach, and the symmetrical uncertainty method were employed to extract the most informative features to train the models.

RESULTS

The RF and SVM models generated using diverse feature types in the present study were highly accurate as is evident from good values for different statistical measures. Moreover, independent test sets and benchmark validations indicated that the proposed method clearly outperformed the existing methods, demonstrating its ability to accurately predict protein phosphorylation.

CONCLUSIONS

The results obtained in the present work indicate that the proposed computational methodology can be effectively used for predicting putative phosphorylation sites further facilitating discovery of various biological processes mechanisms.

Master Blaster: an approach to sensitive identification of remotely related proteins

Genome sequencing projects unearth sequences of all the protein sequences encoded in a genome. As the first step, homology detection is employed to obtain clues to structure and function of these proteins. However, high evolutionary divergence between homologous proteins challenges our ability to detect distant relationships. In the past, an approach involving multiple Position Specific Scoring Matrices (PSSMs) was found to be more effective than traditional single PSSMs. Cascaded search is another successful approach where hits of a search are queried to detect more homologues. We propose a protocol, ‘Master Blaster’, which combines the principles adopted in these two approaches to enhance our ability to detect remote homologues even further. Assessment of the approach was performed using known relationships available in the SCOP70 database, and the results were compared against that of PSI-BLAST and HHblits, a hidden Markov model-based method. Compared to PSI-BLAST, Master Blaster resulted in 10% improvement with respect to detection of cross superfamily connections, nearly 35% improvement in cross family and more than 80% improvement in intra family connections. From the results it was observed that HHblits is more sensitive in detecting remote homologues compared to Master Blaster. However, there are true hits from 46-folds for which Master Blaster reported homologs that are not reported by HHblits even using the optimal parameters indicating that for detecting remote homologues, use of multiple methods employing a combination of different approaches can be more effective in detecting remote homologs. Master Blaster stand-alone code is available for download in the supplementary archive.

The intrinsically disordered protein SPE-18 promotes localized assembly of MSP in Caenorhabditis elegans spermatocytes

Many specialized cells use unconventional strategies of cytoskeletal control. Nematode spermatocytes discard their actin and tubulin following meiosis, and instead employ the regulated assembly/disassembly of the Major Sperm Protein (MSP) to drive sperm motility. However, prior to the meiotic divisions, MSP is sequestered through its assembly into paracrystalline structures called fibrous bodies (FBs). The accessory proteins that direct this sequestration process have remained mysterious. This study reveals SPE-18 as an intrinsically disordered protein that is essential for MSP assembly within FBs. In spe-18 mutant spermatocytes, MSP forms disorganized cortical fibers, and the cells arrest in meiosis without forming haploid sperm. In wild-type spermatocytes, SPE-18 localizes to pre-FB complexes and functions with the kinase SPE-6 to localize MSP assembly. Changing patterns of SPE-18 localization uncover previously unappreciated complexities in FB maturation. Later, within newly individualized spermatids, SPE-18 is rapidly lost, yet SPE-18 loss alone is insufficient for MSP disassembly. Our findings reveal an alternative strategy for sequestering cytoskeletal elements, not as monomers but in localized, bundled polymers. Additionally, these studies provide an important example of disordered proteins promoting ordered cellular structures.

The DBSAV Database: Predicting Deleteriousness of Single Amino Acid Variations in the Human Proteome

Deleterious single amino acid variation (SAV) is one of the leading causes of human diseases. Evaluating the functional impact of SAVs is crucial for diagnosis of genetic disorders. We previously developed a deep convolutional neural network predictor, DeepSAV, to evaluate the deleterious effects of SAVs on protein function based on various sequence, structural, and functional properties. DeepSAV scores of rare SAVs observed in the human population are aggregated into a gene-level score called GTS (Gene Tolerance of rare SAVs) that reflects a gene's tolerance to deleterious missense mutations and serves as a useful tool to study gene-disease associations. In this study, we aim to enhance the performance of DeepSAV by using expanded datasets of pathogenic and benign variants, more features, and neural network optimization. We found that multiple sequence alignments built from vertebrate-level orthologs yield better prediction results compared to those built from mammalian-level orthologs. For multiple sequence alignments built from BLAST searches, optimal performance was achieved with a sequence identify cutoff of 50% to remove distant homologs. The new version of DeepSAV exhibits the best performance among standalone predictors of deleterious effects of SAVs. We developed the DBSAV database (http://prodata.swmed.edu/DBSAV) that reports GTS scores of human genes and DeepSAV scores of SAVs in the human proteome, including pathogenic and benign SAVs, population-level SAVs, and all possible SAVs by single nucleotide variations. This database serves as a useful resource for research of human SAVs and their relationships with protein functions and human diseases.

Structural Insights into the Interaction of the Intrinsically Disordered Co-activator TIF2 with Retinoic Acid Receptor Heterodimer (RXR/RAR)

Retinoic acid receptors (RARs) and retinoid X receptors (RXRs) form heterodimers that activate target gene transcription by recruiting co-activator complexes in response to ligand binding. The nuclear receptor (NR) co-activator TIF2 mediates this recruitment by interacting with the ligand-binding domain (LBD) of NRs trough the nuclear receptor interaction domain (TIF2_NRID) containing three highly conserved α-helical LxxLL motifs (NR-boxes). The precise binding mode of this domain to RXR/RAR is not clear due to the disordered nature of TIF2. Here we present the structural characterization of TIF2_NRID by integrating several experimental (NMR, SAXS, Far-UV CD, SEC-MALS) and computational data. Collectively, the data are in agreement with a largely disordered protein with partially structured regions, including the NR-boxes and their flanking regions, which are evolutionary conserved. NMR and X-ray crystallographic data on TIF2_NRID in complex with RXR/RAR reveal a multisite binding of the three NR-boxes as well as an active role of their flanking regions in the interaction.

Synergistic role of nucleotides and lipids for the self-assembly of Shs1 septin oligomers

Budding yeast septins are essential for cell division and polarity. Septins assemble as palindromic linear octameric complexes. The function and ultra-structural organization of septins are finely governed by their molecular polymorphism. In particular, in budding yeast, the end subunit can stand either as Shs1 or Cdc11. We have dissected, here, for the first time, the behavior of the Shs1 protomer bound to membranes at nanometer resolution, in complex with the other septins. Using electron microscopy, we have shown that on membranes, Shs1 protomers self-assemble into rings, bundles, filaments or two-dimensional gauzes. Using a set of specific mutants we have demonstrated a synergistic role of both nucleotides and lipids for the organization and oligomerization of budding yeast septins. Besides, cryo-electron tomography assays show that vesicles are deformed by the interaction between Shs1 oligomers and lipids. The Shs1-Shs1 interface is stabilized by the presence of phosphoinositides, allowing the visualization of micrometric long filaments formed by Shs1 protomers. In addition, molecular modeling experiments have revealed a potential molecular mechanism regarding the selectivity of septin subunits for phosphoinositide lipids.

Genomic mutations and changes in protein secondary structure and solvent accessibility of SARS-CoV-2 (COVID-19 virus)

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly pathogenic virus that has caused the global COVID-19 pandemic. Tracing the evolution and transmission of the virus is crucial to respond to and control the pandemic through appropriate intervention strategies. This paper reports and analyses genomic mutations in the coding regions of SARS-CoV-2 and their probable protein secondary structure and solvent accessibility changes, which are predicted using deep learning models. Prediction results suggest that mutation D614G in the virus spike protein, which has attracted much attention from researchers, is unlikely to make changes in protein secondary structure and relative solvent accessibility. Based on 6324 viral genome sequences, we create a spreadsheet dataset of point mutations that can facilitate the investigation of SARS-CoV-2 in many perspectives, especially in tracing the evolution and worldwide spread of the virus. Our analysis results also show that coding genes E, M, ORF6, ORF7a, ORF7b and ORF10 are most stable, potentially suitable to be targeted for vaccine and drug development.

ADDRESS: A Database of Disease-associated Human Variants Incorporating Protein Structure and Folding Stabilities

Numerous human diseases are caused by mutations in genomic sequences. Since amino acid changes affect protein function through mechanisms often predictable from protein structure, the integration of structural and sequence data enables us to estimate with greater accuracy whether and how a given mutation will lead to disease. Publicly available annotated databases enable hypothesis assessment and benchmarking of prediction tools. However, the results are often presented as summary statistics or black box predictors, without providing full descriptive information. We developed a new semi-manually curated human variant database presenting information on the protein contact-map, sequence-to-structure mapping, amino acid identity change, and stability prediction for the popular UniProt database. We found that the profiles of pathogenic and benign missense polymorphisms can be effectively deduced using decision trees and comparative analyses based on the presented dataset. The database is made publicly available through https://zhanglab.ccmb.med.umich.edu/ADDRESS.

Molecular Basis of the Anticancer and Antibacterial Properties of CecropinXJ Peptide: An In Silico Study

Esophageal cancer is an aggressive lethal malignancy causing thousands of deaths every year. While current treatments have poor outcomes, cecropinXJ (CXJ) is one of the very few peptides with demonstrated in vivo activity. The great interest in CXJ stems from its low toxicity and additional activity against most ESKAPE bacteria and fungi. Here, we present the first study of its mechanism of action based on molecular dynamics (MD) simulations and sequence-property alignment. Although unstructured in solution, predictions highlight the presence of two helices separated by a flexible hinge containing P24 and stabilized by the interaction of W2 with target biomembranes: an amphipathic helix-I and a poorly structured helix-II. Both MD and sequence-property alignment point to the important role of helix I in both the activity and the interaction with biomembranes. MD reveals that CXJ interacts mainly with phosphatidylserine (PS) but also with phosphatidylethanolamine (PE) headgroups, both found in the outer leaflet of cancer cells, while salt bridges with phosphate moieties are prevalent in bacterial biomimetic membranes composed of PE, phosphatidylglycerol (PG) and cardiolipin (CL). The antibacterial activity of CXJ might also explain its interaction with mitochondria, whose phospholipid composition recalls that of bacteria and its capability to induce apoptosis in cancer cells.

FUpred: detecting protein domains through deep-learning-based contact map prediction

MOTIVATION

Protein domains are subunits that can fold and function independently. Correct domain boundary assignment is thus a critical step toward accurate protein structure and function analyses. There is, however, no efficient algorithm available for accurate domain prediction from sequence. The problem is particularly challenging for proteins with discontinuous domains, which consist of domain segments that are separated along the sequence.

RESULTS

We developed a new algorithm, FUpred, which predicts protein domain boundaries utilizing contact maps created by deep residual neural networks coupled with coevolutionary precision matrices. The core idea of the algorithm is to retrieve domain boundary locations by maximizing the number of intra-domain contacts, while minimizing the number of inter-domain contacts from the contact maps. FUpred was tested on a large-scale dataset consisting of 2549 proteins and generated correct single- and multi-domain classifications with a Matthew's correlation coefficient of 0.799, which was 19.1% (or 5.3%) higher than the best machine learning (or threading)-based method. For proteins with discontinuous domains, the domain boundary detection and normalized domain overlapping scores of FUpred were 0.788 and 0.521, respectively, which were 17.3% and 23.8% higher than the best control method. The results demonstrate a new avenue to accurately detect domain composition from sequence alone, especially for discontinuous, multi-domain proteins.

AVAILABILITY AND IMPLEMENTATION

https://zhanglab.ccmb.med.umich.edu/FUpred.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.