Protein structure prediction and structural genomics

ResiRole: residue-level functional site predictions to gauge the accuracies of protein structure prediction techniques

Motivation

Methods to assess the quality of protein structure models are needed for user applications. To aid with the selection of structure models and further inform the development of structure prediction techniques, we describe the ResiRole method for the assessment of the quality of structure models.

Results

Structure prediction techniques are ranked according to the results of round-robin, head-to-head comparisons using difference scores. Each difference score was defined as the absolute value of the cumulative probability for a functional site prediction made with the FEATURE program for the reference structure minus that for the structure model. Overall, the difference scores correlate well with other model quality metrics; and based on benchmarking studies with NaïveBLAST, they are found to detect additional local structural similarities between the structure models and reference structures.

Availabilityand implementation

Automated analyses of models addressed in CAMEO are available via the ResiRole server, URL http://protein.som.geisinger.edu/ResiRole/. Interactive analyses with user-provided models and reference structures are also enabled. Code is available at github.com/wamclaughlin/ResiRole.

Supplementary information

Supplementary data are available at Bioinformatics online.

Computational studies of the mitochondrial carrier family SLC25. Present status and future perspectives

The members of the mitochondrial carrier family, also known as solute carrier family 25 (SLC25), are transmembrane proteins involved in the translocation of a plethora of small molecules between the mitochondrial intermembrane space and the matrix. These transporters are characterized by three homologous domains structure and a transport mechanism that involves the transition between different conformations. Mutations in regions critical for these transporters’ function often cause several diseases, given the crucial role of these proteins in the mitochondrial homeostasis. Experimental studies can be problematic in the case of membrane proteins, in particular concerning the characterization of the structure–function relationships. For this reason, computational methods are often applied in order to develop new hypotheses or to support/explain experimental evidence. Here the computational analyses carried out on the SLC25 members are reviewed, describing the main techniques used and the outcome in terms of improved knowledge of the transport mechanism. Potential future applications on this protein family of more recent and advanced in silico methods are also suggested.

Revisiting Evaluation of Multiple Sequence Alignment Methods

Multiple sequence alignment is a core first step in many bioinformatics analyses, and errors in these alignments can have negative consequences for scientific studies. In this article, we review some of the recent literature evaluating multiple sequence alignment methods and identify specific challenges that arise when performing these evaluations. In particular, we discuss the different trends observed in simulation studies and when using biological benchmarks. Overall, we find that multiple sequence alignment, far from being a "solved problem," would benefit from new attention.

A colorimetric and electrochemical dual-mode biosensor for thrombin using a magnetic separation technique

In general, protein detection relies primarily on enzyme-linked immunosorbent assays. Here, we constructed a colorimetric and electrochemical dual-mode biosensor for thrombin detection based on the mechanism of aptamer recognition. Magnetic nanobeads (MBs) were used as carriers for separation and enrichment to quickly capture thrombin (TB) in the complex matrix. Also, the combination of MBs and the magnetic electrode array (MEA) effectively avoided the poisoning of the electrode by biological samples. Furthermore, hybridization chain reaction (HCR) was indirectly used to achieve amplification of TB. A large number of horseradish peroxidases (HRPs) were coupled with the amplified long nucleic acid fragments. Based on the color and current response of the substrate TMB catalyzed by HRP, a dual-mode detection system for thrombin was established to ensure the accuracy of the test results. The method had a minimum resolution of 10 nM to the naked eye and an electrochemical detection limit as low as 0.35 nM. In addition, the sensor provided good anti-interference ability in a complex matrix and showed great potential to detect TB in complex samples.

Protein modeling

Proteins are essential and versatile polymers consisting of sequenced amino acids that often possess an organized three-dimensional arrangement, (a result of their monomeric composition), which determines their biological role in cellular function. Proteins are involved in enzymatic catalysis; they participate in genetic information decoding and transmission processes, in cell recognition, in signaling, and transport of substances, in regulation of intra and extracellular conditions, and other functions.

Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks

The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by end-to-end training of deep residual neural-networks. Compared to previous approaches, the major advantage of TripletRes is in its ability to learn and directly fuse a triplet of coevolutionary matrices extracted from the whole-genome and metagenome databases and therefore minimize the information loss during the course of contact model training. TripletRes was tested on a large set of 245 non-homologous proteins from CASP 11&12 and CAMEO experiments and outperformed other top methods from CASP12 by at least 58.4% for the CASP 11&12 targets and 44.4% for the CAMEO targets in the top-L long-range contact precision. On the 31 FM targets from the latest CASP13 challenge, TripletRes achieved the highest precision (71.6%) for the top-L/5 long-range contact predictions. It was also shown that a simple re-training of the TripletRes model with more proteins can lead to further improvement with precisions comparable to state-of-the-art methods developed after CASP13. These results demonstrate a novel efficient approach to extend the power of deep convolutional networks for high-accuracy medium- and long-range protein contact-map predictions starting from primary sequences, which are critical for constructing 3D structure of proteins that lack homologous templates in the PDB library.

Ab initio protein folding has been a major unsolved problem in computational biology for more than half a century. Recent community-wide Critical Assessment of Structure Prediction (CASP) experiments have witnessed exciting progress on ab initio structure prediction, which was mainly powered by the boosting of contact-map prediction as the latter can be used as constraints to guide ab initio folding simulations. In this work, we proposed a new open-source deep-learning architecture, TripletRes, built on the residual convolutional neural networks for high-accuracy contact prediction. The large-scale benchmark and blind test results demonstrate competitive performance of the proposed methods to other top approaches in predicting medium- and long-range contact-maps that are critical for guiding protein folding simulations. Detailed data analyses showed that the major advantage of TripletRes lies in the unique protocol to fuse multiple evolutionary feature matrices which are directly extracted from whole-genome and metagenome databases and therefore minimize the information loss during the contact model training.

In silico modeling of PAX8-PPARγ fusion protein in thyroid carcinoma: influence of structural perturbation by fusion on ligand-binding affinity

Paired box 8 (PAX8)-peroxisome proliferator-activated receptor γ (PPARγ) rearrangement is believed to play an important role in tumorigenesis of PAX8-PPARγ fusion protein (PPFP) thyroid carcinomas, while without establishing any standard treatment, including drugs. Although PPFP is a potential promising target for therapeutic agents, the three-dimensional (3D) structure and functions have not yet been experimentally elucidated. In this study, we aimed to construct the 3D structure of PPFP and to aid in the development of therapies that can target PPFP for thyroid carcinomas. The 3D structure of PPFP was constructed by homology modeling based on crystallographic structure data. To validate the modeled structure, we analyzed the thermal fluctuations by molecular dynamics simulations and predicted the physical properties using bioinformatic analyses. We found that the modeled structure was stable under hydrated conditions and had features indicating the actual existence of the structure. Furthermore, the binding free energies of the ligand rosiglitazone with PPARγ and PPFP were evaluated by the molecular mechanics-Poisson-Boltzmann surface area method. We found that rosiglitazone has different binding affinities for the same binding pockets of PPARγ and PPFP, and the optimal compound for PPFP can differ from that of PPARγ. This suggests the need for the development of drugs targeting PPFP that allow for the fusion, rather than focusing on the PPARγ side of PPFP and searching for the best compounds for that pocket. Our findings are expected to lead to the development of new therapies for thyroid tumors.

The impact of structural bioinformatics tools and resources on SARS-CoV-2 research and therapeutic strategies

SARS-CoV-2 is the causative agent of COVID-19, the ongoing global pandemic. It has posed a worldwide challenge to human health as no effective treatment is currently available to combat the disease. Its severity has led to unprecedented collaborative initiatives for therapeutic solutions against COVID-19. Studies resorting to structure-based drug design for COVID-19 are plethoric and show good promise. Structural biology provides key insights into 3D structures, critical residues/mutations in SARS-CoV-2 proteins, implicated in infectivity, molecular recognition and susceptibility to a broad range of host species. The detailed understanding of viral proteins and their complexes with host receptors and candidate epitope/lead compounds is the key to developing a structure-guided therapeutic design. Since the discovery of SARS-CoV-2, several structures of its proteins have been determined experimentally at an unprecedented speed and deposited in the Protein Data Bank. Further, specialized structural bioinformatics tools and resources have been developed for theoretical models, data on protein dynamics from computer simulations, impact of variants/mutations and molecular therapeutics. Here, we provide an overview of ongoing efforts on developing structural bioinformatics tools and resources for COVID-19 research. We also discuss the impact of these resources and structure-based studies, to understand various aspects of SARS-CoV-2 infection and therapeutic development. These include (i) understanding differences between SARS-CoV-2 and SARS-CoV, leading to increased infectivity of SARS-CoV-2, (ii) deciphering key residues in the SARS-CoV-2 involved in receptor-antibody recognition, (iii) analysis of variants in host proteins that affect host susceptibility to infection and (iv) analyses facilitating structure-based drug and vaccine design against SARS-CoV-2.

Bioinformatic Analysis of the Nicotinamide Binding Site in Poly(ADP-Ribose) Polymerase Family Proteins

Simple Summary

The PARP family consists of 17 proteins, and some of them are responsible for cancer cells’ viability. Much attention is therefore given to the search for chemical compounds with the ability to suppress distinct PARP family members (for example, PARP-5a and 5b). Here, we present the results of a family-wide bioinformatic analysis of an important functional region in the PARP structure and describe factors that can guide the design of highly selective compounds.

Abstract

The PARP family consists of 17 members with diverse functions, including those related to cancer cells’ viability. Several PARP inhibitors are of great interest as innovative anticancer drugs, but they have low selectivity towards distinct PARP family members and exert serious adverse effects. We describe a family-wide study of the nicotinamide (NA) binding site, an important functional region in the PARP structure, using comparative bioinformatic analysis and molecular modeling. Mutations in the NA site and D-loop mobility around the NA site were identified as factors that can guide the design of selective PARP inhibitors. Our findings are of particular importance for the development of novel tankyrase (PARPs 5a and 5b) inhibitors for cancer therapy.

Rotaxane rings promote oblique packing and extended lifetimes in DNA-templated molecular dye aggregates

Molecular excitons play a central role in natural and artificial light harvesting, organic electrònics, and nanoscale computing. The structure and dynamics of molecular excitons, critical to each application, are sensitively governed by molecular packing. Deoxyribonucleic acid (DNA) templating is a powerful approach that enables controlled aggregation via sub-nanometer positioning of molecular dyes. However, finer sub-Angstrom control of dye packing is needed to tailor excitonic properties for specific applications. Here, we show that adding rotaxane rings to squaraine dyes templated with DNA promotes an elusive oblique packing arrangement with highly desirable optical properties. Specifically, dimers of these squaraine:rotaxanes exhibit an absorption spectrum with near-equal intensity excitonically split absorption bands. Theoretical analysis indicates that the transitions are mostly electronic in nature and only have similar intensities over a narrow range of packing angles. Compared with squaraine dimers, squaraine:rotaxane dimers also exhibit extended excited-state lifetimes and less structural heterogeneity. The approach proposed here may be generally useful for optimizing excitonic materials for a variety of applications ranging from solar energy conversion to quantum information science.

SAINT: self-attention augmented inception-inside-inception network improves protein secondary structure prediction

MOTIVATION

Protein structures provide basic insight into how they can interact with other proteins, their functions and biological roles in an organism. Experimental methods (e.g. X-ray crystallography and nuclear magnetic resonance spectroscopy) for predicting the secondary structure (SS) of proteins are very expensive and time consuming. Therefore, developing efficient computational approaches for predicting the SS of protein is of utmost importance. Advances in developing highly accurate SS prediction methods have mostly been focused on 3-class (Q3) structure prediction. However, 8-class (Q8) resolution of SS contains more useful information and is much more challenging than the Q3 prediction.

RESULTS

We present SAINT, a highly accurate method for Q8 structure prediction, which incorporates self-attention mechanism (a concept from natural language processing) with the Deep Inception-Inside-Inception network in order to effectively capture both the short- and long-range interactions among the amino acid residues. SAINT offers a more interpretable framework than the typical black-box deep neural network methods. Through an extensive evaluation study, we report the performance of SAINT in comparison with the existing best methods on a collection of benchmark datasets, namely, TEST2016, TEST2018, CASP12 and CASP13. Our results suggest that self-attention mechanism improves the prediction accuracy and outperforms the existing best alternate methods. SAINT is the first of its kind and offers the best known Q8 accuracy. Thus, we believe SAINT represents a major step toward the accurate and reliable prediction of SSs of proteins.

AVAILABILITY AND IMPLEMENTATION

SAINT is freely available as an open-source project at https://github.com/SAINTProtein/SAINT.

Improved Sampling Strategies for Protein Model Refinement Based on Molecular Dynamics Simulation

Protein structures provide valuable information for understanding biological processes. Protein structures can be determined by experimental methods such as X-ray crystallography, nuclear magnetic resonance spectroscopy, or cryogenic electron microscopy. As an alternative, in silico methods can be used to predict protein structures. These methods utilize protein structure databases for structure prediction via template-based modeling or for training machine-learning models to generate predictions. Structure prediction for proteins distant from proteins with known structures often results in lower accuracy with respect to the true physiological structures. Physics-based protein model refinement methods can be applied to improve model accuracy in the predicted models. Refinement methods rely on conformational sampling around the predicted structures, and if structures closer to the native states are sampled, improvements in the model quality become possible. Molecular dynamics simulations have been especially successful for improving model qualities but although consistent refinement can be achieved, the improvements in model qualities are still moderate. To extend the refinement performance of a simulation-based protocol, we explored new schemes that focus on optimized use of biasing functions and the application of increased simulation temperatures. In addition, we tested the use of alternative initial models so that the simulations can explore the conformational space more broadly. Based on the insights of this analysis, we are proposing a new refinement protocol that significantly outperformed previous state-of-the-art molecular dynamics simulation-based protocols in the benchmark tests described here.

Survey of Similarity-Based Prediction of Drug-Protein Interactions

Therapeutic activity of a significant majority of drugs is determined by their interactions with proteins. Databases of drug-protein interactions (DPIs) primarily focus on the therapeutic protein targets while the knowledge of the off-targets is fragmented and partial. One way to bridge this knowledge gap is to employ computational methods to predict protein targets for a given drug molecule, or interacting drugs for given protein targets. We survey a comprehensive set of 35 methods that were published in high-impact venues and that predict DPIs based on similarity between drugs and similarity between protein targets. We analyze the internal databases of known PDIs that these methods utilize to compute similarities, and investigate how they are linked to the 12 publicly available source databases. We discuss contents, impact and relationships between these internal and source databases, and well as the timeline of their releases and publications. The 35 predictors exploit and often combine three types of similarities that consider drug structures, drug profiles, and target sequences. We review the predictive architectures of these methods, their impact, and we explain how their internal DPIs databases are linked to the source databases. We also include a detailed timeline of the development of these predictors and discuss the underlying limitations of the current resources and predictive tools. Finally, we provide several recommendations concerning the future development of the related databases and methods.

Conserved sequence motifs in human TMTC1, TMTC2, TMTC3, and TMTC4, new O-mannosyltransferases from the GT-C/PMT clan, are rationalized as ligand binding sites

BACKGROUND

The human proteins TMTC1, TMTC2, TMTC3 and TMTC4 have been experimentally shown to be components of a new O-mannosylation pathway. Their own mannosyl-transferase activity has been suspected but their actual enzymatic potential has not been demonstrated yet. So far, sequence analysis of TMTCs has been compromised by evolutionary sequence divergence within their membrane-embedded N-terminal region, sequence inaccuracies in the protein databases and the difficulty to interpret the large functional variety of known homologous proteins (mostly sugar transferases and some with known 3D structure).

RESULTS

Evolutionary conserved molecular function among TMTCs is only possible with conserved membrane topology within their membrane-embedded N-terminal regions leading to the placement of homologous long intermittent loops at the same membrane side. Using this criterion, we demonstrate that all TMTCs have 11 transmembrane regions. The sequence segment homologous to Pfam model DUF1736 is actually just a loop between TM7 and TM8 that is located in the ER lumen and that contains a small hydrophobic, but not membrane-embedded helix. Not only do the membrane-embedded N-terminal regions of TMTCs share a common fold and 3D structural similarity with subgroups of GT-C sugar transferases. The conservation of residues critical for catalysis, for binding of a divalent metal ion and of the phosphate group of a lipid-linked sugar moiety throughout enzymatically and structurally well-studied GT-Cs and sequences of TMTCs indicates that TMTCs are actually sugar-transferring enzymes. We present credible 3D structural models of all four TMTCs (derived from their closest known homologues 5ezm/5f15) and find observed conserved sequence motifs rationalized as binding sites for a metal ion and for a dolichyl-phosphate-mannose moiety.

CONCLUSIONS

With the results from both careful sequence analysis and structural modelling, we can conclusively say that the TMTCs are enzymatically active sugar transferases belonging to the GT-C/PMT superfamily. The DUF1736 segment, the loop between TM7 and TM8, is critical for catalysis and lipid-linked sugar moiety binding. Together with the available indirect experimental data, we conclude that the TMTCs are not only part of an O-mannosylation pathway in the endoplasmic reticulum of upper eukaryotes but, actually, they are the sought mannosyl-transferases.

Protein Structure Modeling with MODELLER

Genome sequencing projects have resulted in a rapid increase in the number of known protein sequences. In contrast, only about one-hundredth of these sequences have been characterized at atomic resolution using experimental structure determination methods. Computational protein structure modeling techniques have the potential to bridge this sequence-structure gap. In the following chapter, we present an example that illustrates the use of MODELLER to construct a comparative model for a protein with unknown structure. Automation of a similar protocol has resulted in models of useful accuracy for domains in more than half of all known protein sequences.

Preclinical development of a humanized chimeric antigen receptor against B cell maturation antigen for multiple myeloma

Multiple myeloma is a prevalent and incurable disease, despite the development of new and effective drugs. The recent development of chimeric antigen receptor (CAR)-T cell therapy has shown impressive results in the treatment of patients with relapsed or refractory hematological B cell malignancies. In the recent years, B-cell maturation antigen (BCMA) has appeared as a promising antigen to target using a variety of immuno-therapy treatments including CART cells, for MM patients. To this end, we generated clinical-grade murine CART cells directed against BCMA, named ARI2m cells. Having demonstrated its efficacy, and in an attempt to avoid the immune rejection of CART cells by the patient, the single chain variable fragment was humanized, creating ARI2h cells. ARI2h cells demonstrated comparable in vitro and in vivo efficacy to ARI2m cells, and superiority in cases of high tumor burden disease. In terms of inflammatory response, ARI2h cells showed a lower TNFα production and lower in vivo toxicity profile. Large-scale expansion of both ARI2m and ARI2h cells was efficiently conducted following Good Manufacturing Practice guidelines, obtaining the target CART cell dose required for treatment of multiple myeloma patients. Moreover, we demonstrate that soluble BCMA and BCMA released in vesicles impacts on CAR-BCMA activity. In summary, this study sets the bases for the implementation of a clinical trial (EudraCT code: 2019-001472-11) to study the efficacy of ARI2h cell treatment for multiple myeloma patients.

De Novo Protein Design for Novel Folds Using Guided Conditional Wasserstein Generative Adversarial Networks

Although massive data is quickly accumulating on protein sequence and structure, there is a small and limited number of protein architectural types (or structural folds). This study is addressing the following question: how well could one reveal underlying sequence-structure relationships and design protein sequences for an arbitrary, potentially novel, structural fold? In response to the question, we have developed novel deep generative models, namely, semisupervised gcWGAN (guided, conditional, Wasserstein Generative Adversarial Networks). To overcome training difficulties and improve design qualities, we build our models on conditional Wasserstein GAN (WGAN) that uses Wasserstein distance in the loss function. Our major contributions include (1) constructing a low-dimensional and generalizable representation of the fold space for the conditional input, (2) developing an ultrafast sequence-to-fold predictor (or oracle) and incorporating its feedback into WGAN as a loss to guide model training, and (3) exploiting sequence data with and without paired structures to enable a semisupervised training strategy. Assessed by the oracle over 100 novel folds not in the training set, gcWGAN generates more successful designs and covers 3.5 times more target folds compared to a competing data-driven method (cVAE). Assessed by sequence- and structure-based predictors, gcWGAN designs are physically and biologically sound. Assessed by a structure predictor over representative novel folds, including one not even part of basis folds, gcWGAN designs have comparable or better fold accuracy yet much more sequence diversity and novelty than cVAE. The ultrafast data-driven model is further shown to boost the success of a principle-driven de novo method (RosettaDesign), through generating design seeds and tailoring design space. In conclusion, gcWGAN explores uncharted sequence space to design proteins by learning generalizable principles from current sequence-structure data. Data, source codes, and trained models are available at https://github.com/Shen-Lab/gcWGAN.

Structural modelling and thermostability of a serine protease inhibitor belonging to the Kunitz-BPTI family from the Rhipicephalus microplus tick

rBmTI-A is a recombinant serine protease inhibitor that belongs to the Kunitz-BPTI family and that was cloned from Rhipicephalus microplus tick. rBmTI-A has inhibitory activities on bovine trypsin, human plasma kallikrein, human neutrophil elastase and plasmin with dissociation constants in nM range. It is characterized by two inhibitory domains and each domain presents six cysteines that form three disulfide bonds, which contribute to the high stability of its structure. Previous studies suggest that serine protease inhibitor rBmTI-A has a protective potential against pulmonary emphysema in mice and anti-inflammatory potential. Besides that, rBmTI-A presented a potent inhibitory activity against in vitro vessel formation. In this study, the tertiary structure of rBmTI-A was modeled. The structure stabilization was evaluated by molecular dynamics analysis. Circular dichroism spectroscopy data corroborated the secondary structure found by the homology modelling. Also, in circular dichroism data it was shown a thermostability of rBmTI-A until approximately 70 °C, corroborated by inhibitory assays toward trypsin.

Bioinformatics as a Tool for the Structural and Evolutionary Analysis of Proteins

This chapter deals with the topic of bioinformatics, computational, mathematics, and statistics tools applied to biology, essential for the analysis and characterization of biological molecules, in particular proteins, which play an important role in all cellular and evolutionary processes of the organisms. In recent decades, with the next generation sequencing technologies and bioinformatics, it has facilitated the collection and analysis of a large amount of genomic, transcriptomic, proteomic, and metabolomic data from different organisms that have allowed predictions on the regulation of expression, transcription, translation, structure, and mechanisms of action of proteins as well as homology, mutations, and evolutionary processes that generate structural and functional changes over time. Although the information in the databases is greater every day, all bioinformatics tools continue to be constantly modified to improve performance that leads to more accurate predictions regarding protein functionality, which is why bioinformatics research remains a great challenge.

Genetic interaction mapping informs integrative structure determination of protein complexes

Determining structures of protein complexes is crucial for understanding cellular functions. Here, we describe an integrative structure determination approach that relies on in vivo measurements of genetic interactions. We construct phenotypic profiles for point mutations crossed against gene deletions or exposed to environmental perturbations, followed by converting similarities between two profiles into an upper bound on the distance between the mutated residues. We determine the structure of the yeast histone H3-H4 complex based on ~500,000 genetic interactions of 350 mutants. We then apply the method to subunits Rpb1-Rpb2 of yeast RNA polymerase II and subunits RpoB-RpoC of bacterial RNA polymerase. The accuracy is comparable to that based on chemical cross-links; using restraints from both genetic interactions and cross-links further improves model accuracy and precision. The approach provides an efficient means to augment integrative structure determination with in vivo observations.

Using Integrative Modeling Platform to compute, validate, and archive a model of a protein complex structure

Biology is advanced by producing structural models of biological systems, such as protein complexes. Some systems are recalcitrant to traditional structure determination methods. In such cases, it may still be possible to produce useful models by integrative structure determination that depends on simultaneous use of multiple types of data. An ensemble of models that are sufficiently consistent with the data is produced by a structural sampling method guided by a data-dependent scoring function. The variation in the ensemble of models quantified the uncertainty of the structure, generally resulting from the uncertainty in the input information and actual structural heterogeneity in the samples used to produce the data. Here, we describe how to generate, assess, and interpret ensembles of integrative structural models using our open source Integrative Modeling Platform program (https://integrativemodeling.org).

A single-model quality assessment method for poor quality protein structure

Background

Quality assessment of protein tertiary structure prediction models, in which structures of the best quality are selected from decoys, is a major challenge in protein structure prediction, and is crucial to determine a model’s utility and potential applications. Estimating the quality of a single model predicts the model’s quality based on the single model itself. In general, the Pearson correlation value of the quality assessment method increases in tandem with an increase in the quality of the model pool. However, there is no consensus regarding the best method to select a few good models from the poor quality model pool.

Results

We introduce a novel single-model quality assessment method for poor quality models that uses simple linear combinations of six features. We perform weighted search and linear regression on a large dataset of models from the 12th Critical Assessment of Protein Structure Prediction (CASP12) and benchmark the results on CASP13 models. We demonstrate that our method achieves outstanding performance on poor quality models.

Conclusions

According to results of poor protein structure assessment based on six features, contact prediction and relying on fewer prediction features can improve selection accuracy.

Anti-Allergic Potential of Cinnamaldehyde via the Inhibitory Effect of Histidine Decarboxylase (HDC) Producing Klebsiella pneumonia

Allergy is an immunological disorder that develops in response to exposure to an allergen, and histamines mediate these effects via histidine decarboxylase (HDC) activity at the intracellular level. In the present study, we developed a 3D model of Klebsiella pneumoniae histidine decarboxylase (HDC) and analyzed the HDC inhibitory potential of cinnamaldehyde (CA) and subsequent anti-allergic potential using a bacterial and mammalian mast cell model. A computational and in vitro study using K. pneumonia revealed that CA binds to HDC nearby the pyridoxal-5'-phosphate (PLP) binding site and inhibited histamine synthesis in a bacterial model. Further study using a mammalian mast cell model also showed that CA decreased the levels of histamine in the stimulated RBL-2H3 cell line and attenuated the release of β-hexoseaminidase and cell degranulation. In addition, CA treatment also significantly suppressed the levels of pro-inflammatory cytokines TNF-α and IL-6 and the nitric oxide (NO) level in the stimulated mast cells. A gene expression and Western blotting study revealed that CA significantly downregulated the expressions of MAPKp38/ERK and its downstream pro-allergic mediators that are involved in the signaling pathway in mast cell cytokine synthesis. This study further confirms that CA has the potential to attenuate mast cell activation by inhibiting HDC and modifying the process of allergic disorders.

New Antibacterial Peptides from the Freshwater Mollusk Pomacea poeyana (Pilsbry, 1927)

Antimicrobial peptides (AMPs) are biomolecules with antimicrobial activity against a broad group of pathogens. In the past few decades, AMPs have represented an important alternative for the treatment of infectious diseases. Their isolation from natural sources has been widely investigated. In this sense, mollusks are promising organisms for the identification of AMPs given that their immune system mainly relies on innate response. In this report, we characterized the peptide fraction of the Cuban freshwater snail Pomacea poeyana (Pilsbry, 1927) and identified 37 different peptides by nanoLC-ESI-MS-MS technology. From these peptide sequences, using bioinformatic prediction tools, we discovered two potential antimicrobial peptides named Pom-1 (KCAGSIAWAIGSGLFGGAKLIKIKKYIAELGGLQ) and Pom-2 (KEIERAGQRIRDAIISAAPAVETLAQAQKIIKGG). Database search revealed that Pom-1 is a fragment of Closticin 574 previously isolated from the bacteria Clostridium tyrobutyrium, and Pom-2 is a fragment of cecropin D-like peptide first isolated from Galleria mellonella hemolymph. These sequences were chemically synthesized and evaluated against different human pathogens. Interestingly, structural predictions of both peptides in the presence of micelles showed models that comprise two alpha helices joined by a short loop. The CD spectra analysis of Pom-1 and Pom-2 in water showed for both structures a high random coil content, a certain content of α-helix and a low β-sheet content. Like other described AMPs displaying a disordered structure in water, the peptides may adopt a helical conformation in presence of bacterial membranes. In antimicrobial assays, Pom-1 demonstrated high activity against the Gram-negative bacteria Pseudomonas aeruginosa and moderate activity against Klebsiella pneumoniae and Listeria monocytogenes. Neither of the two peptides showed antifungal action. Pom-1 moderately inhibits Zika Virus infection but slightly enhances HIV-1 infectivion in vitro. The evaluation of cell toxicity on primary human macrophages did not show toxicity on THP-1 cells, although slight overall toxicity was observed in high concentrations of Pom-1. We assume that both peptides may play a key role in innate defense of P. poeyana and represent promising antimicrobial candidates for humans.

QMEANDisCo-distance constraints applied on model quality estimation

Motivation

Methods that estimate the quality of a 3D protein structure model in absence of an experimental reference structure are crucial to determine a model’s utility and potential applications. Single model methods assess individual models whereas consensus methods require an ensemble of models as input. In this work, we extend the single model composite score QMEAN that employs statistical potentials of mean force and agreement terms by introducing a consensus-based distance constraint (DisCo) score.

Results

DisCo exploits distance distributions from experimentally determined protein structures that are homologous to the model being assessed. Feed-forward neural networks are trained to adaptively weigh contributions by the multi-template DisCo score and classical single model QMEAN parameters. The result is the composite score QMEANDisCo, which combines the accuracy of consensus methods with the broad applicability of single model approaches. We also demonstrate that, despite being the de-facto standard for structure prediction benchmarking, CASP models are not the ideal data source to train predictive methods for model quality estimation. For performance assessment, QMEANDisCo is continuously benchmarked within the CAMEO project and participated in CASP13. For both, it ranks among the top performers and excels with low response times.

Availability and implementation

QMEANDisCo is available as web-server at https://swissmodel.expasy.org/qmean. The source code can be downloaded from https://git.scicore.unibas.ch/schwede/QMEAN.

Supplementary information

Supplementary data are available at Bioinformatics online.

A novel bacteriocin BM1029: physicochemical characterization, antibacterial modes and application

AIM

Bacteriocins with antimicrobial activity are considered as potential natural bio-preservatives to control the growth of food spoilage bacteria. The aim of this work was to characterize a novel bacteriocin BM1029 discovered from Lactobacillus crustorum MN047 and evaluate its antibacterial mechanism.

METHODS AND RESULTS

Bacteriocin BM1029 was purified by cation-exchange chromatography and reversed-phase chromatography. Antibacterial activity assay showed that BM1029 is antagonistic against both Gram-positive and Gram-negative bacteria. Furthermore, it was found that BM1029 showed low haemolysis with high stability to the pretreatment with different temperatures, pH and surfactants. Moreover electron microscopy and flow cytometry suggested that BM1029 inhibit indicator strains by damaging the cell envelope integrity. Cell cycle assay suggested that BM1029 arrested cell cycle in R-phase.

CONCLUSION

The novel bacteriocin BM1029 showed high bactericidal activity against Escherichia coli and Staphylococcus aureus through a cell envelope-associated mechanism.

SIGNIFICANCE AND IMPACT OF THE STUDY

Application of BM1029 inhibited the growth of indicator strains on beef meat storage at 4°C suggesting that this bacteriocin is promising to be used as a novel preservative in food processing and preservation.

An overview of in silico vaccine design against different pathogens and cancer

INTRODUCTION

Due to overcome the hardness of the vaccine design, computational vaccinology is emerging widely. Prediction of T cell and B cell epitopes, antigen processing analysis, antigenicity analysis, population coverage, conservancy analysis, allergenicity assessment, toxicity prediction, and protein-peptide docking are important steps in the process of designing and developing potent vaccines against various viruses and cancers. In order to perform all of the analyses, several bioinformatics tools and online web servers have been developed. Scientists must take the decision to apply more suitable and precise servers for each part based on their accuracy.

AREAS COVERED

In this review, a wide-range list of different bioinformatics tools and online web servers has been provided. Moreover, some studies were proposed to show the importance of various bioinformatics tools for predicting and developing efficient vaccines against different pathogens including viruses, bacteria, parasites, and fungi as well as cancer.

EXPERT OPINION

Immunoinformatics is the best way to find potential vaccine candidates against different pathogens. Thus, the selection of the most accurate tools is necessary to predict and develop potent preventive and therapeutic vaccines. To further evaluation of the computational and in silico vaccine design, in vitro/in vivo analyses are required to develop vaccine candidates.

Homology modeling and in silico design of novel and potential dual-acting inhibitors of human histone deacetylases HDAC5 and HDAC9 isozymes

Histone deacetylases (HDACs) are a group of enzymes that have prominent and crucial effect on various biological systems, mainly by their suppressive effect on transcription. Searching for inhibitors targeting their respective isoforms without affecting other targets is greatly needed. Some histone deacetylases have no crystal structures, such as HDAC5 and HDAC9. Lacking proper and suitable crystal structure is obstructing the designing of appropriate isoform selective inhibitors. Here in this study, we constructed human HDAC5 and HDAC9 protein models using human HDAC4 (PDB:2VQM_A) as a template by the means of homology modeling approach. Based on the Z-score of the built models, model M0014 of HDAC5 and model M0020 of HDAC9 were selected. The models were verified by MODELLER and validated using the Web-based PROCHECK server. All selected known inhibitors displayed reasonable binding modes and equivalent predicted Ki values in comparison to the experimental binding affinities (Ki/IC50). The known inhibitor Rac26 showed the best binding affinity for HDAC5, while TMP269 showed the best binding affinity for HDAC9. The best two compounds, CHEMBL2114980 and CHEMBL217223, had relatively similar inhibition constants against HDAC5 and HDAC9. The built models and their complexes were subjected to molecular dynamic simulations (MD) for 100 ns. Examining the MD simulation results of all studied structures, including the RMSD, RMSF, radius of gyration and potential energy suggested the stability and reliability of the built models. Accordingly, the results obtained in this study could be used for designing de novo inhibitors against HDAC5 and HDAC9. Communicated by Ramaswamy H. Sarma.

Comparative bioinformatic and structural analyses of pepsin and renin

Pepsin, the archetypal pepsin-like aspartic protease, is irreversibly denatured when exposed to neutral pH conditions whereas renin, a structural homologue of pepsin, is fully stable and optimally active in the same conditions despite sharing highly similar enzyme architecture. To gain insight into the structural determinants of differential aspartic protease pH stability, the present study used comparative bioinformatic and structural analyses. In pepsin, an abundance of polar and aspartic acid residues were identified, a common trait with other acid-stable enzymes. Conversely, renin was shown to have increased levels of basic amino acids. In both pepsin and renin, the solvent exposure of these charged groups was high. Having similar overall acidic residue content, the solvent-exposed basic residues may allow for extensive salt bridge formation in renin, whereas in pepsin, these residues are protonated and serve to form stabilizing hydrogen bonds at low pH. Relative differences in structure and sequence in the turn and joint regions of the β-barrel and ψ-loop in both the N- and C-terminal lobes were identified as regions of interest in defining divergent pH stability. Compared to the structural rigidity of renin, pepsin has more instability associated with the N-terminus, specifically the B/C connector. By contrast, renin exhibits greater C-terminal instability in turn and connector regions. Overall, flexibility differences in connector regions, and amino acid composition, particularly in turn and joint regions of the β-barrel and ψ-loops, likely play defining roles in determining pH stability for renin and pepsin.

Development of A4 antibody for detection of neuraminidase I223R/H275Y-associated antiviral multidrug-resistant influenza virus

The emergence and spread of antiviral drug-resistant viruses have been a worldwide challenge and a great concern for patient care. We report A4 antibody specifically recognizing and binding to the mutant I223R/H275Y neuraminidase and prove the applicability of A4 antibody for direct detection of antiviral multidrug-resistant viruses in various sensing platforms, including naked-eye detection, surface-enhanced Raman scattering-based immunoassay, and lateral flow system. The development of the A4 antibody enables fast, simple, and reliable point-of-care assays of antiviral multidrug-resistant influenza viruses. In addition to current influenza virus infection testing methods that do not provide information on the antiviral drug-resistance of the virus, diagnostic tests for antiviral multidrug-resistant viruses will improve clinical judgment in the treatment of influenza virus infections, avoid the unnecessary prescription of ineffective drugs, and improve current therapies.

A Divergent Articulavirus in an Australian Gecko Identified Using Meta-Transcriptomics and Protein Structure Comparisons

The discovery of highly divergent RNA viruses is compromised by their limited sequence similarity to known viruses. Evolutionary information obtained from protein structural modelling offers a powerful approach to detect distantly related viruses based on the conservation of tertiary structures in key proteins such as the RNA-dependent RNA polymerase (RdRp). We utilised a template-based approach for protein structure prediction from amino acid sequences to identify distant evolutionary relationships among viruses detected in meta-transcriptomic sequencing data from Australian wildlife. The best predicted protein structural model was compared with the results of similarity searches against protein databases. Using this combination of meta-transcriptomics and protein structure prediction we identified the RdRp (PB1) gene segment of a divergent negative-sense RNA virus, denoted Lauta virus (LTAV), in a native Australian gecko (Gehyra lauta). The presence of this virus was confirmed by PCR and Sanger sequencing. Phylogenetic analysis revealed that Lauta virus likely represents a newly described genus within the family Amnoonviridae, order Articulavirales, that is most closely related to the fish virus Tilapia tilapinevirus (TiLV). These findings provide important insights into the evolution of negative-sense RNA viruses and structural conservation of the viral replicase among members of the order Articulavirales.

Experimentally-driven protein structure modeling

Revolutions in natural and exact sciences started at the dawn of last century have led to the explosion of theoretical, experimental, and computational approaches to determine structures of molecules, complexes, as well as their rich conformational dynamics. Since different experimental methods produce information that is attributed to specific time and length scales, corresponding computational methods have to be tailored to these scales and experiments. These methods can be then combined and integrated in scales, hence producing a fuller picture of molecular structure and motion from the "puzzle pieces" offered by various experiments. Here, we describe a number of computational approaches to utilize experimental data to glance into structure of proteins and understand their dynamics. We will also discuss the limitations and the resolution of the constraints-based modeling approaches. SIGNIFICANCE: Experimentally-driven computational structure modeling and determination is a rapidly evolving alternative to traditional approaches for molecular structure determination. These new hybrid experimental-computational approaches are proving to be a powerful microscope to glance into the structural features of intrinsically or partially disordered proteins, dynamics of molecules and complexes. In this review, we describe various approaches in the field of experimentally-driven computational structure modeling.

Mass Spectrometry-Based Protein Footprinting for Higher-Order Structure Analysis: Fundamentals and Applications

Proteins adopt different higher-order structures (HOS) to enable their unique biological functions. Understanding the complexities of protein higher-order structures and dynamics requires integrated approaches, where mass spectrometry (MS) is now positioned to play a key role. One of those approaches is protein footprinting. Although the initial demonstration of footprinting was for the HOS determination of protein/nucleic acid binding, the concept was later adapted to MS-based protein HOS analysis, through which different covalent labeling approaches "mark" the solvent accessible surface area (SASA) of proteins to reflect protein HOS. Hydrogen-deuterium exchange (HDX), where deuterium in D2O replaces hydrogen of the backbone amides, is the most common example of footprinting. Its advantage is that the footprint reflects SASA and hydrogen bonding, whereas one drawback is the labeling is reversible. Another example of footprinting is slow irreversible labeling of functional groups on amino acid side chains by targeted reagents with high specificity, probing structural changes at selected sites. A third footprinting approach is by reactions with fast, irreversible labeling species that are highly reactive and footprint broadly several amino acid residue side chains on the time scale of submilliseconds. All of these covalent labeling approaches combine to constitute a problem-solving toolbox that enables mass spectrometry as a valuable tool for HOS elucidation. As there has been a growing need for MS-based protein footprinting in both academia and industry owing to its high throughput capability, prompt availability, and high spatial resolution, we present a summary of the history, descriptions, principles, mechanisms, and applications of these covalent labeling approaches. Moreover, their applications are highlighted according to the biological questions they can answer. This review is intended as a tutorial for MS-based protein HOS elucidation and as a reference for investigators seeking a MS-based tool to address structural questions in protein science.

Structural Biology in the Multi-Omics Era

Rapid developments in cryogenic electron microscopy have opened new avenues to probe the structures of protein assemblies in their near native states. Recent studies have begun applying single -particle analysis to heterogeneous mixtures, revealing the potential of structural-omics approaches that combine the power of mass spectrometry and electron microscopy. Here we highlight advances and challenges in sample preparation, data processing, and molecular modeling for handling increasingly complex mixtures. Such advances will help structural-omics methods extend to cellular-level models of structural biology.

Protein Structure Prediction in CASP13 Using AWSEM-Suite

Recently several techniques have emerged that significantly enhance the quality of predictions of protein tertiary structures. In this study, we describe the performance of AWSEM-Suite, an algorithm that incorporates template-based modeling and coevolutionary restraints with a realistic coarse-grained force field, AWSEM. With its roots in neural networks, AWSEM contains both physical and bioinformatical energies that have been optimized using energy landscape theory. AWSEM-Suite participated in CASP13 as a server predictor and generated reliable predictions for most targets. AWSEM-Suite ranked eighth in both the free-modeling category and the hard-to-model category and in one case provided the best submitted prediction. Here we critically discuss the prediction performance of AWSEM-Suite using several examples from different categories in CASP13. Structure prediction tests on these selected targets, two of them being hard-to-model targets, show that AWSEM-Suite can achieve high-resolution structure prediction after incorporating both template guidances and coevolutionary restraints even when homology is weak. For targets with reliable templates (template-easy category), introducing coevolutionary restraints sometimes damages the overall quality of the predictions. Free energy profile analyses demonstrate, however, that the incorporations of both of these evolutionarily informed terms effectively increase the funneling of the landscape toward native-like structures while still allowing sufficient flexibility to correct for discrepancies between the correct target structure and the provided guidance. In contrast to other predictors that are exclusively oriented toward structure prediction, the connection of AWSEM-Suite to a statistical mechanical basis and affiliated molecular dynamics and importance sampling simulations makes it suitable for functional explorations.

VoroMQA web server for assessing three-dimensional structures of proteins and protein complexes

The VoroMQA (Voronoi tessellation-based Model Quality Assessment) web server is dedicated to the estimation of protein structure quality, a common step in selecting realistic and most accurate computational models and in validating experimental structures. As an input, the VoroMQA web server accepts one or more protein structures in PDB format. Input structures may be either monomeric proteins or multimeric protein complexes. For every input structure, the server provides both global and local (per-residue) scores. Visualization of the local scores along the protein chain is enhanced by providing secondary structure assignment and information on solvent accessibility. A unique feature of the VoroMQA server is the ability to directly assess protein-protein interaction interfaces. If this type of assessment is requested, the web server provides interface quality scores, interface energy estimates, and local scores for residues involved in inter-chain interfaces. VoroMQA, the underlying method of the web server, was extensively tested in recent community-wide CASP and CAPRI experiments. During these experiments VoroMQA showed outstanding performance both in model selection and in estimation of accuracy of local structural regions. The VoroMQA web server is available at http://bioinformatics.ibt.lt/wtsam/voromqa.

Graph-Directed Approach for Downselecting Toxins for Experimental Structure Determination

Conotoxins are short, cysteine-rich peptides of great interest as novel therapeutic leads and of great concern as lethal biological agents due to their high affinity and specificity for various receptors involved in neuromuscular transmission. Currently, of the approximately 6000 known conotoxin sequences, only about 3% have associated structural characterization, which leads to a bottleneck in rapid high-throughput screening (HTS) for identification of potential leads or threats. In this work, we combine a graph-based approach with homology modeling to expand the library of conotoxin structures and to identify those conotoxin sequences that are of the greatest value for experimental structural characterization. The latter would allow for the rapid expansion of the known structural space for generating high quality template-based models. Our approach generalizes to other evolutionarily-related, short, cysteine-rich venoms of interest. Overall, we present and validate an approach for venom structure modeling and experimental guidance and employ it to produce a 290%-larger library of approximate conotoxin structures for HTS. We also provide a set of ranked conotoxin sequences for experimental structure determination to further expand this library.

Structural elements that modulate the substrate specificity of plant purple acid phosphatases: Avenues for improved phosphorus acquisition in crops

Phosphate acquisition by plants is an essential process that is directly implicated in the optimization of crop yields. Purple acid phosphatases (PAPs) are ubiquitous metalloenzymes, which catalyze the hydrolysis of a wide range of phosphate esters and anhydrides. While some plant PAPs display a preference for ATP as the substrate, others are efficient in hydrolyzing phytate or 2-phosphoenolpyruvate (PEP). PAP from red kidney bean (rkbPAP) is an efficient ATP- and ADPase, but has no activity towards phytate. Crystal structures of this enzyme in complex with ATP analogues (to 2.20 and 2.60 Å resolution, respectively) complement the recent structure of rkbPAP with a bound ADP analogue (ChemBioChem 20 (2019) 1536). Together these complexes provide the first structural insight of a PAP in complex with molecules that mimic biologically relevant substrates. Homology modeling was used to generate three-dimensional structures for the active sites of PAPs from tobacco (NtPAP) and thale cress (AtPAP26) that are efficient in hydrolyzing phytate and PEP as preferred substrates, respectively. The combining of crystallographic data, substrate docking simulations and a phylogenetic analysis of 49 plant PAP sequences (including the first PAP sequences reported from Eucalyptus) resulted in the identification of several active site residues that are important in defining the substrate specificities of plant PAPs; of particular relevance is the identification of a motif ("REKA") that is characteristic for plant PAPs that possess phytase activity. These results may inform bioengineering studies aimed at identifying and incorporating suitable plant PAP genes into crops to improve phosphorus acquisition and use efficiency. Organic phosphorus sources increasingly supplement or replace inorganic fertilizer, and efficient phosphorus use of crops will lower the environmental footprint of agriculture while enhancing food production.

Residue Cluster Classes: A Unified Protein Representation for Efficient Structural and Functional Classification

Proteins are characterized by their structures and functions, and these two fundamental aspects of proteins are assumed to be related. To model such a relationship, a single representation to model both protein structure and function would be convenient, yet so far, the most effective models for protein structure or function classification do not rely on the same protein representation. Here we provide a computationally efficient implementation for large datasets to calculate residue cluster classes (RCCs) from protein three-dimensional structures and show that such representations enable a random forest algorithm to effectively learn the structural and functional classifications of proteins, according to the CATH and Gene Ontology criteria, respectively. RCCs are derived from residue contact maps built from different distance criteria, and we show that 7 or 8 Å with or without amino acid side-chain atoms rendered the best classification models. The potential use of a unified representation of proteins is discussed and possible future areas for improvement and exploration are presented.

Performance of virtual screening against GPCR homology models: Impact of template selection and treatment of binding site plasticity

Rational drug design for G protein-coupled receptors (GPCRs) is limited by the small number of available atomic resolution structures. We assessed the use of homology modeling to predict the structures of two therapeutically relevant GPCRs and strategies to improve the performance of virtual screening against modeled binding sites. Homology models of the D2 dopamine (D2R) and serotonin 5-HT2A receptors (5-HT2AR) were generated based on crystal structures of 16 different GPCRs. Comparison of the homology models to D2R and 5-HT2AR crystal structures showed that accurate predictions could be obtained, but not necessarily using the most closely related template. Assessment of virtual screening performance was based on molecular docking of ligands and decoys. The results demonstrated that several templates and multiple models based on each of these must be evaluated to identify the optimal binding site structure. Models based on aminergic GPCRs showed substantial ligand enrichment and there was a trend toward improved virtual screening performance with increasing binding site accuracy. The best models even yielded ligand enrichment comparable to or better than that of the D2R and 5-HT2AR crystal structures. Methods to consider binding site plasticity were explored to further improve predictions. Molecular docking to ensembles of structures did not outperform the best individual binding site models, but could increase the diversity of hits from virtual screens and be advantageous for GPCR targets with few known ligands. Molecular dynamics refinement resulted in moderate improvements of structural accuracy and the virtual screening performance of snapshots was either comparable to or worse than that of the raw homology models. These results provide guidelines for successful application of structure-based ligand discovery using GPCR homology models.

Using Complementary Methods of Synchrotron Radiation Powder Diffraction and Pair Distribution Function to Refine Crystal Structures with High Quality Parameters—A Review

Determination of the atomic-scale structures of certain fine-grained minerals using single-crystal X-ray diffraction (XRD) has been challenging because they commonly occur as submicron and nanocrystals in the geological environment. Synchrotron powder diffraction and scattering techniques are useful complementary methods for studying this type of minerals. In this review, we discussed three example studies investigated by combined methods of synchrotron radiation XRD and pair distribution function (PDF) techniques: (1) low-temperature cristobalite; (2) kaolinite; and (3) vernadite. Powder XRD is useful to determine the average structure including unit-cell parameters, fractional atomic coordinates, occupancies and isotropic atomic displacement parameters. X-ray/Neutron PDF methods are sensitive to study the local structure with anisotropic atomic displacement parameters (ADP). The results and case studies suggest that the crystal structure and high-quality ADP values can be obtained using the combined methods. The method can be useful to characterize crystals and minerals that are not suitable for single-crystal XRD.

A Functional Kinase Is Necessary for Cyclin-Dependent Kinase G1 (CDKG1) to Maintain Fertility at High Ambient Temperature in Arabidopsis

Maintaining fertility in a fluctuating environment is key to the reproductive success of flowering plants. Meiosis and pollen formation are particularly sensitive to changes in growing conditions, especially temperature. We have previously identified cyclin-dependent kinase G1 (CDKG1) as a master regulator of temperature-dependent meiosis and this may involve the regulation of alternative splicing (AS), including of its own transcript. CDKG1 mRNA can undergo several AS events, potentially producing two protein variants: CDKG1L and CDKG1S, differing in their N-terminal domain which may be involved in co-factor interaction. In leaves, both isoforms have distinct temperature-dependent functions on target mRNA processing, but their role in pollen development is unknown. In the present study, we characterize the role of CDKG1L and CDKG1S in maintaining Arabidopsis fertility. We show that the long (L) form is necessary and sufficient to rescue the fertility defects of the cdkg1-1 mutant, while the short (S) form is unable to rescue fertility. On the other hand, an extra copy of CDKG1L reduces fertility. In addition, mutation of the ATP binding pocket of the kinase indicates that kinase activity is necessary for the function of CDKG1. Kinase mutants of CDKG1L and CDKG1S correctly localize to the cell nucleus and nucleus and cytoplasm, respectively, but are unable to rescue either the fertility or the splicing defects of the cdkg1-1 mutant. Furthermore, we show that there is partial functional overlap between CDKG1 and its paralog CDKG2 that could in part be explained by overlapping gene expression.