Recent Progress of Protein Tertiary Structure Prediction.

The prediction of three-dimensional (3D) protein structure from amino acid sequences has stood as a significant challenge in computational and structural bioinformatics for decades. Recently, the widespread integration of artificial intelligence (AI) algorithms has substantially expedited advancements in protein structure prediction, yielding numerous significant milestones. In particular, the end-to-end deep learning method AlphaFold2 has facilitated the rise of structure prediction performance to new heights, regularly competitive with experimental structures in the 14th Critical Assessment of Protein Structure Prediction (CASP14). To provide a comprehensive understanding and guide future research in the field of protein structure prediction for researchers, this review describes various methodologies, assessments, and databases in protein structure prediction, including traditionally used protein structure prediction methods, such as template-based modeling (TBM) and template-free modeling (FM) approaches; recently developed deep learning-based methods, such as contact/distance-guided methods, end-to-end folding methods, and protein language model (PLM)-based methods; multi-domain protein structure prediction methods; the CASP experiments and related assessments; and the recently released AlphaFold Protein Structure Database (AlphaFold DB). We discuss their advantages, disadvantages, and application scopes, aiming to provide researchers with insights through which to understand the limitations, contexts, and effective selections of protein structure prediction methods in protein-related fields.

Adaptive evolution and co-evolution of chloroplast genomes in Pteridaceae species occupying different habitats: overlapping residues are always highly mutated.

BACKGROUND: The evolution of protein residues depends on the mutation rates of their encoding nucleotides, but it may also be affected by co-evolution with other residues. Chloroplasts function as environmental sensors, transforming fluctuating environmental signals into different physiological responses. We reasoned that habitat diversity may affect their rate and mode of evolution, which might be evidenced in the chloroplast genome. The Pteridaceae family of ferns occupy an unusually broad range of ecological niches, which provides an ideal system for analysis. RESULTS: We conducted adaptive evolution and intra-molecular co-evolution analyses of Pteridaceae chloroplast DNAs (cpDNAs). The results indicate that the residues undergoing adaptive evolution and co-evolution were mostly independent, with only a few residues being simultaneously involved in both processes, and these overlapping residues tend to exhibit high mutations. Additionally, our data showed that Pteridaceae chloroplast genes are under purifying selection. Regardless of whether we grouped species by lineage (which corresponded with ecological niches), we determined that positively selected residues mainly target photosynthetic genes. CONCLUSIONS: Our work provides evidence for the adaptive evolution of Pteridaceae cpDNAs, especially photosynthetic genes, to different habitats and sheds light on the adaptive evolution and co-evolution of proteins.

CRISPR-Cas-Docker: web-based in silico docking and machine learning-based classification of crRNAs with Cas proteins.

BACKGROUND: CRISPR-Cas-Docker is a web server for in silico docking experiments with CRISPR RNAs (crRNAs) and Cas proteins. This web server aims at providing experimentalists with the optimal crRNA-Cas pair predicted computationally when prokaryotic genomes have multiple CRISPR arrays and Cas systems, as frequently observed in metagenomic data. RESULTS: CRISPR-Cas-Docker provides two methods to predict the optimal Cas protein given a particular crRNA sequence: a structure-based method (in silico docking) and a sequence-based method (machine learning classification). For the structure-based method, users can either provide experimentally determined 3D structures of these macromolecules or use an integrated pipeline to generate 3D-predicted structures for in silico docking experiments. CONCLUSION: CRISPR-Cas-Docker addresses the need of the CRISPR-Cas community to predict RNA-protein interactions in silico by optimizing multiple stages of computation and evaluation, specifically for CRISPR-Cas systems. CRISPR-Cas-Docker is available at www.crisprcasdocker.org as a web server, and at https://github.com/hshimlab/CRISPR-Cas-Docker as an open-source tool.

The Venturia inaequalis effector repertoire is dominated by expanded families with predicted structural similarity, but unrelated sequence, to avirulence proteins from other plant-pathogenic fungi.

BACKGROUND: Scab, caused by the biotrophic fungus Venturia inaequalis, is the most economically important disease of apples worldwide. During infection, V. inaequalis occupies the subcuticular environment, where it secretes virulence factors, termed effectors, to promote host colonization. Consistent with other plant-pathogenic fungi, many of these effectors are expected to be non-enzymatic proteins, some of which can be recognized by corresponding host resistance proteins to activate plant defences, thus acting as avirulence determinants. To develop durable control strategies against scab, a better understanding of the roles that these effector proteins play in promoting subcuticular growth by V. inaequalis, as well as in activating, suppressing, or circumventing resistance protein-mediated defences in apple, is required. RESULTS: We generated the first comprehensive RNA-seq transcriptome of V. inaequalis during colonization of apple. Analysis of this transcriptome revealed five temporal waves of gene expression that peaked during early, mid, or mid-late infection. While the number of genes encoding secreted, non-enzymatic proteinaceous effector candidates (ECs) varied in each wave, most belonged to waves that peaked in expression during mid-late infection. Spectral clustering based on sequence similarity determined that the majority of ECs belonged to expanded protein families. To gain insights into function, the tertiary structures of ECs were predicted using AlphaFold2. Strikingly, despite an absence of sequence similarity, many ECs were predicted to have structural similarity to avirulence proteins from other plant-pathogenic fungi, including members of the MAX, LARS, ToxA and FOLD effector families. In addition, several other ECs, including an EC family with sequence similarity to the AvrLm6 avirulence effector from Leptosphaeria maculans, were predicted to adopt a KP6-like fold. Thus, proteins with a KP6-like fold represent another structural family of effectors shared among plant-pathogenic fungi. CONCLUSIONS: Our study reveals the transcriptomic profile underpinning subcuticular growth by V. inaequalis and provides an enriched list of ECs that can be investigated for roles in virulence and avirulence. Furthermore, our study supports the idea that numerous sequence-unrelated effectors across plant-pathogenic fungi share common structural folds. In doing so, our study gives weight to the hypothesis that many fungal effectors evolved from ancestral genes through duplication, followed by sequence diversification, to produce sequence-unrelated but structurally similar proteins.

Characterization of two conserved cell death elicitor families from the Dothideomycete fungal pathogens Dothistroma septosporum and Fulvia fulva (syn. Cladosporium fulvum).

Dothistroma septosporum (Ds) and Fulvia fulva (Ff; previously called Cladosporium fulvum) are two closely related Dothideomycete fungal species that cause Dothistroma needle blight in pine and leaf mold in tomato, respectively. During host colonization, these pathogens secrete virulence factors termed effectors to promote infection. In the presence of corresponding host immune receptors, however, these effectors activate plant defenses, including a localized cell death response that halts pathogen growth. We identified two apoplastic effector protein families, Ecp20 and Ecp32, which are conserved between the two pathogens. The Ecp20 family has four paralogues in both species, while the Ecp32 family has four paralogues in D. septosporum and five in F. fulva. Both families have members that are highly expressed during host infection. Members of the Ecp20 family have predicted structural similarity to proteins with a ß-barrel fold, including the Alt a 1 allergen from Alternaria alternata, while members of the Ecp32 family have predicted structural similarity to proteins with a ß-trefoil fold, such as trypsin inhibitors and lectins. Using Agrobacterium tumefaciens-mediated transient transformation assays, each family member was assessed for its ability to trigger cell death in leaves of the non-host species Nicotiana benthamiana and N. tabacum. Using this approach, FfEcp20-2, DsEcp20-3, and FfEcp20-3 from the Ecp20 family, and all members from the Ecp32 family, except for the Ds/FfEcp32-4 pair, triggered cell death in both species. This cell death was dependent on secretion of the effectors to the apoplast. In line with recognition by an extracellular immune receptor, cell death triggered by Ds/FfEcp20-3 and FfEcp32-3 was compromised in N. benthamiana silenced for BAK1 or SOBIR1, which encode extracellular co-receptors involved in transducing defense response signals following apoplastic effector recognition. We then investigated whether DsEcp20-3 and DsEcp20-4 triggered cell death in the host species Pinus radiata by directly infiltrating purified protein into pine needles. Strikingly, as in the non-host species, DsEcp20-3 triggered cell death, while DsEcp20-4 did not. Collectively, our study describes two new candidate effector families with cell death-eliciting activity from D. septosporum and F. fulva and provides evidence that members of these families are recognized by plant immune receptors.

Protein sequence profile prediction using ProtAlbert transformer.

Profiles are used to model protein families and domains. They are built by multiple sequence alignments obtained by mapping a query sequence against a database to generate a profile based on the substitution scoring matrix. The profile applications are very dependent on the alignment algorithm and scoring system for amino acid substitution. However, sometimes there are no similar sequences in the database with the query sequence based on the scoring schema. In these cases, it is not possible to make a profile. This paper proposes a method named PA_SPP, based on pre-trained ProtAlbert transformer to predict the profile for a single protein sequence without alignment. The performance of transformers on natural languages is impressive. Protein sequences can be viewed as a language; we can benefit from these models. We analyze the attention heads in different layers of ProtAlbert to show that the transformer can capture five essential protein characteristics of a single sequence. This assessment shows that ProtAlbert considers some protein properties when suggesting amino acids for each position in the sequence. In other words, transformers can be considered an appropriate alternative for alignment and scoring schema to predict a profile. We evaluate PA_SPP on the Casp13 dataset, including 55 proteins. Meanwhile, one thermophilic and two mesophilic proteins are used as case studies. The results display high similarity between the predicted profiles and HSSP profiles.

Different methods, techniques and their limitations in protein structure prediction: A review.

Because of the increase in different types of diseases in human habitats, demands for designing various types of drugs are also increasing. Protein and its structure play a very important role in drug design. Therefore researchers from different areas like mathematics, medicines, and computer science are teaming up for getting better solutions in the said field. In this paper, we have discussed different methods of secondary and tertiary protein structure prediction (PSP), along with the limitations of different approaches. Different types of datasets used in PSP are also discussed here. This paper also tells about different performance measures to evaluate the prediction accuracy of PSP methods. Different software's/servers are available for download, which are used to find the protein structures for the input protein sequence. These softwares will also help to compare the performance of any new algorithm with other available methods. Details of those softwares are also mentioned in this paper.

Reaction mechanism of tetrathionate hydrolysis based on the crystal structure of tetrathionate hydrolase from Acidithiobacillus ferrooxidans.

Tetrathionate hydrolase (4THase) plays an important role in dissimilatory sulfur oxidation in the acidophilic iron- and sulfur-oxidizing bacterium Acidithiobacillus ferrooxidans. The structure of recombinant 4THase from A. ferrooxidans (Af-Tth) was determined by X-ray crystallography to a resolution of 1.95 Å. Af-Tth is a homodimer, and its monomer structure exhibits an eight-bladed ß-propeller motif. Two insertion loops participate in dimerization, and one loop forms a cavity with the ß-propeller region. We observed unexplained electron densities in this cavity of the substrate-soaked structure. The anomalous difference map generated using diffraction data collected at a wavelength of 1.9 Å indicated the presence of polymerized sulfur atoms. Asp325, a highly conserved residue among 4THases, was located near the polymerized sulfur atoms. 4THase activity was completely abolished in the site-specific Af-Tth D325N variant, suggesting that Asp325 plays a crucial role in the first step of tetrathionate hydrolysis. Considering that the Af-Tth reaction occurs only under acidic pH, Asp325 acts as an acid for the tetrathionate hydrolysis reaction. The polymerized sulfur atoms in the active site cavity may represent the intermediate product in the subsequent step.

Identification of continuous immunoglobulin G epitopes of Dermatophagoides farinae allergens by peptide microarray immunoassay.

Dermatophagoides farinae, as a common house dust mite species, is one of the main sources of allergens in the world. At present, Dermatophagoides farinae is found to contain more than 30 groups of allergens. These allergens are used for allergen-specific immunotherapy (AIT) of allergic diseases. During the AIT process, immunoglobulin G (IgG) antibodies can block immunoglobulin E (IgE) antibody-induced allergic reactions in the human body. One of the mechanisms may be that IgG and IgE competitively bind to the same allergic protein, so it is necessary to explore the binding sites (epitopes) of IgG antibodies to allergens. In this study, peptide arrays were constructed to react with the serums from patients with allergic asthma to find the IgG epitopes of several allergens including major allergens (Der f 1, 2) and mid-tier allergens (Der f 4, 5, and 7), and then verified by enzyme-linked immunosorbent assay (ELISA) test. Relevant epitopic sequences were located on the tertiary structure of individual allergens, as reconstructed by homology modeling. One IgG epitope of Der f 1 (90-106aa, NVPSELDLRSLRTVTPI), five IgG epitopes of Der f 4 (61-77aa, ERYQPVSYDIHTRSGDE; 193-209aa, FRSDASTHQWPDDLRSI; 226-242aa, HPFIYHETIYYGGNGIN; 271-287aa, LRWLRNFGTEWGLVPSG; 352-368aa, NDWVGPPTDQHGNILSV), and one IgG epitope of Der f 5 (84-101aa, RYNVEIALKSNEILERDL) were identified. IgG epitopes of Der f 2, 7 were not found. There are overlaps between the IgG and IgE epitopes of Der f 1, 4, and 5. These findings not only reflect the practicality of peptide array and ELISA test in the allergen IgG epitope identification, but also provide more information for further understanding of the human immunological changes during AIT and the molecular mechanisms of IgG blocking IgE activity.

Multidomain protein structure prediction using information about residues interacting on multimeric protein interfaces.

Protein functions can be predicted based on their three-dimensional structures. However, many multidomain proteins have unstable structures, making it difficult to determine the whole structure in biological experiments. Additionally, multidomain proteins are often decomposed and identified based on their domains, with the structure of each domain often found in public databases. Recent studies have advanced structure prediction methods of multidomain proteins through computational analysis. In existing methods, proteins that serve as templates are used for three-dimensional structure prediction. However, when no protein template is available, the accuracy of the prediction is decreased. This study was conducted to predict the structures of multidomain proteins without the need for whole structure templates. We improved structure prediction methods by performing rigid-body docking from the structure of each domain and reranking a structure closer to the correct structure to have a higher value. In the proposed method, the score for the domain-domain interaction obtained without a structural template of the multidomain protein and score for the three-dimensional structure obtained during docking calculation were newly incorporated into the score function. We successfully predicted the structures of 50 of 55 multidomain proteins examined in the test dataset. Interaction residue pair information of the protein-protein complex interface contributes to domain reorganizations even when a structural template for a multidomain protein cannot be obtained. This approach may be useful for predicting the structures of multidomain proteins with important biochemical functions.

Prediction of Protein Tertiary Structure via Regularized Template Classification Techniques.

We discuss the use of the regularized linear discriminant analysis (LDA) as a model reduction technique combined with particle swarm optimization (PSO) in protein tertiary structure prediction, followed by structure refinement based on singular value decomposition (SVD) and PSO. The algorithm presented in this paper corresponds to the category of template-based modeling. The algorithm performs a preselection of protein templates before constructing a lower dimensional subspace via a regularized LDA. The protein coordinates in the reduced spaced are sampled using a highly explorative optimization algorithm, regressive-regressive PSO (RR-PSO). The obtained structure is then projected onto a reduced space via singular value decomposition and further optimized via RR-PSO to carry out a structure refinement. The final structures are similar to those predicted by best structure prediction tools, such as Rossetta and Zhang servers. The main advantage of our methodology is that alleviates the ill-posed character of protein structure prediction problems related to high dimensional optimization. It is also capable of sampling a wide range of conformational space due to the application of a regularized linear discriminant analysis, which allows us to expand the differences over a reduced basis set.

12-Oxophytodienoic Acid Reductase 3 (OPR3) Functions as NADPH-Dependent α,ß-Ketoalkene Reductase in Detoxification and Monodehydroascorbate Reductase in Redox Homeostasis.

Arabidopsis (Arabidopsis thaliana) 12-oxophytodienoic acid reductase isoform 3 (OPR3) is involved in the synthesis of jasmonic acid (JA) by reducing the α,ß-unsaturated double bond of the cyclopentenone moiety in 12-oxophytodienoic acid (12-OPDA). Recent research revealed that JA synthesis is not strictly dependent on the peroxisomal OPR3. The ability of OPR3 to reduce trinitrotoluene suggests that the old yellow enzyme homolog OPR3 has additional functions. Here, we show that OPR3 catalyzes the reduction of a wide spectrum of electrophilic species that share a reactivity toward the major redox buffers glutathione (GSH) and ascorbate (ASC). Furthermore, we show that 12-OPDA reacts with ASC to form an ASC-12-OPDA adduct, but in addition OPR3 has the ability to regenerate ASC from monodehydroascorbate. The presented data characterize OPR3 as a bifunctional enzyme with NADPH-dependent α,ß-ketoalkene double-bond reductase and monodehydroascorbate reductase activities (MDHAR). opr3 mutants showed a slightly less-reduced ASC pool in leaves in line with the MDHAR activity of OPR3 in vitro. These functions link redox homeostasis as mediated by ASC and GSH with OPR3 activity and metabolism of reactive electrophilic species.

Variability of Protein Structure Models from Electron Microscopy.

An increasing number of biomolecular structures are solved by electron microscopy (EM). However, the quality of structure models determined from EM maps vary substantially. To understand to what extent structure models are supported by information embedded in EM maps, we used two computational structure refinement methods to examine how much structures can be refined using a dataset of 49 maps with accompanying structure models. The extent of structure modification as well as the disagreement between refinement models produced by the two computational methods scaled inversely with the global and the local map resolutions. A general quantitative estimation of deviations of structures for particular map resolutions are provided. Our results indicate that the observed discrepancy between the deposited map and the refined models is due to the lack of structural information present in EM maps and thus these annotations must be used with caution for further applications.

An amino acid code to define a protein's tertiary packing surface.

One difficult aspect of the protein-folding problem is characterizing the nonspecific interactions that define packing in protein tertiary structure. To better understand tertiary structure, this work extends the knob-socket model by classifying the interactions of a single knob residue packed into a set of contiguous sockets, or a pocket made up of 4 or more residues. The knob-socket construct allows for a symbolic two-dimensional mapping of pockets. The two-dimensional mapping of pockets provides a simple method to investigate the variety of pocket shapes to understand the geometry of protein tertiary surfaces. The diversity of pocket geometries can be organized into groups of pockets that share a common core, which suggests that some interactions in pockets are ancillary to packing. Further analysis of pocket geometries displays a preferred configuration that is right-handed in α-helices and left-handed in ß-sheets. The amino acid composition of pockets illustrates the importance of nonpolar amino acids in packing as well as position specificity. As expected, all pocket shapes prefer to pack with hydrophobic knobs; however, knobs are not selective for the pockets they pack. Investigating side-chain rotamer preferences for certain pocket shapes uncovers no strong correlations. These findings allow a simple vocabulary based on knobs and sockets to describe protein tertiary packing that supports improved analysis, design, and prediction of protein structure.

Toolbox for Protein Structure Prediction.

Protein tertiary structure prediction algorithms aim to predict, from amino acid sequence, the tertiary structure of a protein. In silico protein structure prediction methods have become extremely important, as in vitro-based structural elucidation is unable to keep pace with the current growth of sequence databases due to high-throughput next-generation sequencing, which has exacerbated the gaps in our knowledge between sequences and structures.Here we briefly discuss protein tertiary structure prediction, the biennial competition for the Critical Assessment of Techniques for Protein Structure Prediction (CASP) and its role in shaping the field. We also discuss, in detail, our cutting-edge web-server method IntFOLD2-TS for tertiary structure prediction. Furthermore, we provide a step-by-step guide on using the IntFOLD2-TS web server, along with some real world examples, where the IntFOLD server can and has been used to improve protein tertiary structure prediction and aid in functional elucidation.

A deep phylogeny of viral and cellular right-hand polymerases.

Right-hand polymerases are important players in genome replication and repair in cellular organisms as well as in viruses. All right-hand polymerases are grouped into seven related protein families: viral RNA-dependent RNA polymerases, reverse transcriptases, single-subunit RNA polymerases, and DNA polymerase families A, B, D, and Y. Although the evolutionary relationships of right-hand polymerases within each family have been proposed, evolutionary relationships between families remain elusive because their sequence similarity is too low to allow classical phylogenetic analyses. The structure of viral RNA-dependent RNA polymerases recently was shown to be useful in inferring their evolution. Here, we address evolutionary relationships between right-hand polymerase families by combining sequence and structure information. We used a set of 22 viral and cellular polymerases representing all right-hand polymerase families with known protein structure. In contrast to previous studies, which focused only on the evolution of particular families, the current approach allowed us to present the first robust phylogenetic analysis unifying evolution of all right-hand polymerase families. All polymerase families branched into discrete lineages, following a fairly robust adjacency pattern. Only single-subunit RNA polymerases formed an inner group within DNA polymerase family A. RNA-dependent RNA polymerases of RNA viruses and reverse transcriptases of retroviruses formed two sister groups and were distinguishable from all other polymerases. DNA polymerases of DNA bacteriophages did not form a monophyletic group and are phylogenetically mixed with cellular DNA polymerase families A and B. Based on the highest genetic variability and structural simplicity, we assume that RNA-dependent RNA polymerases are the most ancient group of right-hand polymerases, in agreement with the RNA World hypothesis, because RNA-dependent RNA polymerases are enzymes that could serve in replication of RNA genomes. Moreover, our results show that protein structure can be used in phylogenetic analyses of distantly related proteins that share only limited sequence similarity.

A homology/ab initio hybrid algorithm for sampling near-native protein conformations.

One of the major challenges for protein tertiary structure prediction strategies is the quality of conformational sampling algorithms, which can effectively and readily search the protein fold space to generate near-native conformations. In an effort to advance the field by making the best use of available homology as well as fold recognition approaches along with ab initio folding methods, we have developed Bhageerath-H Strgen, a homology/ab initio hybrid algorithm for protein conformational sampling. The methodology is tested on the benchmark CASP9 dataset of 116 targets. In 93% of the cases, a structure with TM-score ≥ 0.5 is generated in the pool of decoys. Further, the performance of Bhageerath-H Strgen was seen to be efficient in comparison with different decoy generation methods. The algorithm is web enabled as Bhageerath-H Strgen web tool which is made freely accessible for protein decoy generation (http://www.scfbio-iitd.res.in/software/Bhageerath-HStrgen1.jsp).

Fast and Accurate Calculation of Protein Depth by Euclidean Distance Transform.

The depth of each atom/residue in a protein structure is a key attribution that has been widely used in protein structure modeling and function annotation. However, the accurate calculation of depth is time consuming. Here, we propose to use the Euclidean distance transform (EDT) to calculate the depth, which conveniently converts the protein structure to a 3D gray-scale image with each pixel labeling the minimum distance of the pixel to the surface of the molecule (i.e. the depth). We tested the proposed EDT method on a set of 261 non-redundant protein structures. The data show that the EDT method is 2.6 times faster than the widely used method by Chakravarty and Varadarajan. The depth value by EDT method is also highly accurate, which is almost identical to the depth calculated by exhaustive search (Pearson's correlation coefficient≈1). We believe the EDT-based depth calculation program can be used as an efficient tool to assist the studies of protein fold recognition and structure-based function annotation.

NEW MDS AND CLUSTERING BASED ALGORITHMS FOR PROTEIN MODEL QUALITY ASSESSMENT AND SELECTION.

In protein tertiary structure prediction, assessing the quality of predicted models is an essential task. Over the past years, many methods have been proposed for the protein model quality assessment (QA) and selection problem. Despite significant advances, the discerning power of current methods is still unsatisfactory. In this paper, we propose two new algorithms, CC-Select and MDS-QA, based on multidimensional scaling and k-means clustering. For the model selection problem, CC-Select combines consensus with clustering techniques to select the best models from a given pool. Given a set of predicted models, CC-Select first calculates a consensus score for each structure based on its average pairwise structural similarity to other models. Then, similar structures are grouped into clusters using multidimensional scaling and clustering algorithms. In each cluster, the one with the highest consensus score is selected as a candidate model. For the QA problem, MDS-QA combines single-model scoring functions with consensus to determine more accurate assessment score for every model in a given pool. Using extensive benchmark sets of a large collection of predicted models, we compare the two algorithms with existing state-of-the-art quality assessment methods and show significant improvement.