Protein structure prediction by AlphaFold2: are attention and symmetries all you need?

Reliability of AlphaFold2 Models in Virtual Drug Screening: A Focus on Selected Class A GPCRs

Protein three-dimensional (3D) structure prediction is one of the most challenging issues in the field of computational biochemistry, which has overwhelmed scientists for almost half a century. A significant breakthrough in structural biology has been established by developing the artificial intelligence (AI) system AlphaFold2 (AF2). The AF2 system provides a state-of-the-art prediction of protein structures from nearly all known protein sequences with high accuracy. This study examined the reliability of AF2 models compared to the experimental structures in drug discovery, focusing on one of the most common protein drug-targeted classes known as G protein-coupled receptors (GPCRs) class A. A total of 32 representative protein targets were selected, including experimental structures of X-ray crystallographic and Cryo-EM structures and their corresponding AF2 models. The quality of AF2 models was assessed using different structure validation tools, including the pLDDT score, RMSD value, MolProbity score, percentage of Ramachandran favored, QMEAN Z-score, and QMEANDisCo Global. The molecular docking was performed using the Genetic Optimization for Ligand Docking (GOLD) software. The AF2 models' reliability in virtual drug screening was determined by their ability to predict the ligand binding poses closest to the native binding pose by assessing the Root Mean Square Deviation (RMSD) metric and docking scoring function. The quality of the docking and scoring function was evaluated using the enrichment factor (EF). Furthermore, the capability of using AF2 models in molecular docking to identify hits with key protein-ligand interactions was analyzed. The posing power results showed that the AF2 models successfully predicted ligand binding poses (RMSD < 2 Å). However, they exhibited lower screening power, with average EF values of 2.24, 2.42, and 1.82 for X-ray, Cryo-EM, and AF2 structures, respectively. Moreover, our study revealed that molecular docking using AF2 models can identify competitive inhibitors. In conclusion, this study found that AF2 models provided docking results comparable to experimental structures, particularly for certain GPCR targets, and could potentially significantly impact drug discovery.

Integrating Large-Scale Protein Structure Prediction into Human Genetics Research

The last five years have seen impressive progress in deep learning models applied to protein research. Most notably, sequence-based structure predictions have seen transformative gains in the form of AlphaFold2 and related approaches. Millions of missense protein variants in the human population lack annotations, and these computational methods are a valuable means to prioritize variants for further analysis. Here, we review the recent progress in deep learning models applied to the prediction of protein structure and protein variants, with particular emphasis on their implications for human genetics and health. Improved prediction of protein structures facilitates annotations of the impact of variants on protein stability, protein-protein interaction interfaces, and small-molecule binding pockets. Moreover, it contributes to the study of host-pathogen interactions and the characterization of protein function. As genome sequencing in large cohorts becomes increasingly prevalent, we believe that better integration of state-of-the-art protein informatics technologies into human genetics research is of paramount importance.

The power and pitfalls of AlphaFold2 for structure prediction beyond rigid globular proteins

Artificial intelligence-driven advances in protein structure prediction in recent years have raised the question: has the protein structure-prediction problem been solved? Here, with a focus on nonglobular proteins, we highlight the many strengths and potential weaknesses of DeepMind's AlphaFold2 in the context of its biological and therapeutic applications. We summarize the subtleties associated with evaluation of AlphaFold2 model quality and reliability using the predicted local distance difference test (pLDDT) and predicted aligned error (PAE) values. We highlight various classes of proteins that AlphaFold2 can be applied to and the caveats involved. Concrete examples of how AlphaFold2 models can be integrated with experimental data in the form of small-angle X-ray scattering (SAXS), solution NMR, cryo-electron microscopy (cryo-EM) and X-ray diffraction are discussed. Finally, we highlight the need to move beyond structure prediction of rigid, static structural snapshots toward conformational ensembles and alternate biologically relevant states. The overarching theme is that careful consideration is due when using AlphaFold2-generated models to generate testable hypotheses and structural models, rather than treating predicted models as de facto ground truth structures.

Benchmarking AlphaFold-Generated Structures of Chemokine-Chemokine Receptor Complexes

AlphaFold and AlphaFold-Multimer have become two essential tools for the modeling of unknown structures of proteins and protein complexes. In this work, we extensively benchmarked the quality of chemokine-chemokine receptor structures generated by AlphaFold-Multimer against experimentally determined structures. Our analysis considered both the global quality of the model, as well as key structural features for chemokine recognition. To study the effects of template and multiple sequence alignment parameters on the results, a new prediction pipeline called LIT-AlphaFold (https://github.com/LIT-CCM-lab/LIT-AlphaFold) was developed, allowing extensive input customization. AlphaFold-Multimer correctly predicted differences in chemokine binding orientation and accurately reproduced the unique binding orientation of the CXCL12-ACKR3 complex. Further, the predictions of the full receptor N-terminus provided insights into a putative chemokine recognition site 0.5. The accuracy of chemokine N-terminus binding mode prediction varied between complexes, but the confidence score permitted the distinguishing of residues that were very likely well positioned. Finally, we generated a high-confidence model of the unsolved CXCL12-CXCR4 complex, which agreed with experimental mutagenesis and cross-linking data.

The Potential of Neural Network Potentials

In the next half-century, physical chemistry will likely undergo a profound transformation, driven predominantly by the combination of recent advances in quantum chemistry and machine learning (ML). Specifically, equivariant neural network potentials (NNPs) are a breakthrough new tool that are already enabling us to simulate systems at the molecular scale with unprecedented accuracy and speed, relying on nothing but fundamental physical laws. The continued development of this approach will realize Paul Dirac's 80-year-old vision of using quantum mechanics to unify physics with chemistry and providing invaluable tools for understanding materials science, biology, earth sciences, and beyond. The era of highly accurate and efficient first-principles molecular simulations will provide a wealth of training data that can be used to build automated computational methodologies, using tools such as diffusion models, for the design and optimization of systems at the molecular scale. Large language models (LLMs) will also evolve into increasingly indispensable tools for literature review, coding, idea generation, and scientific writing.

Learning the shape of protein microenvironments with a holographic convolutional neural network

Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure from sequence, determining protein function from its sequence or structure remains a major challenge. Here, we introduce holographic convolutional neural network (H-CNN) for proteins, which is a physically motivated machine learning approach to model amino acid preferences in protein structures. H-CNN reflects physical interactions in a protein structure and recapitulates the functional information stored in evolutionary data. H-CNN accurately predicts the impact of mutations on protein stability and binding of protein complexes. Our interpretable computational model for protein structure-function maps could guide design of novel proteins with desired function.

A Perspective on the Prospective Use of AI in Protein Structure Prediction

AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.

Updated protein domain annotation of the PARP protein family sheds new light on biological function

AlphaFold2 and related computational tools have greatly aided studies of structural biology through their ability to accurately predict protein structures. In the present work, we explored AF2 structural models of the 17 canonical members of the human PARP protein family and supplemented this analysis with new experiments and an overview of recent published data. PARP proteins are typically involved in the modification of proteins and nucleic acids through mono or poly(ADP-ribosyl)ation, but this function can be modulated by the presence of various auxiliary protein domains. Our analysis provides a comprehensive view of the structured domains and long intrinsically disordered regions within human PARPs, offering a revised basis for understanding the function of these proteins. Among other functional insights, the study provides a model of PARP1 domain dynamics in the DNA-free and DNA-bound states and enhances the connection between ADP-ribosylation and RNA biology and between ADP-ribosylation and ubiquitin-like modifications by predicting putative RNA-binding domains and E2-related RWD domains in certain PARPs. In line with the bioinformatic analysis, we demonstrate for the first time PARP14's RNA-binding capability and RNA ADP-ribosylation activity in vitro. While our insights align with existing experimental data and are probably accurate, they need further validation through experiments.

The Realm of Unconventional Noncovalent Interactions in Proteins: Their Significance in Structure and Function

Proteins and their assemblies are fundamental for living cells to function. Their complex three-dimensional architecture and its stability are attributed to the combined effect of various noncovalent interactions. It is critical to scrutinize these noncovalent interactions to understand their role in the energy landscape in folding, catalysis, and molecular recognition. This Review presents a comprehensive summary of unconventional noncovalent interactions, beyond conventional hydrogen bonds and hydrophobic interactions, which have gained prominence over the past decade. The noncovalent interactions discussed include low-barrier hydrogen bonds, C5 hydrogen bonds, C-H···π interactions, sulfur-mediated hydrogen bonds, n → π* interactions, London dispersion interactions, halogen bonds, chalcogen bonds, and tetrel bonds. This Review focuses on their chemical nature, interaction strength, and geometrical parameters obtained from X-ray crystallography, spectroscopy, bioinformatics, and computational chemistry. Also highlighted are their occurrence in proteins or their complexes and recent advances made toward understanding their role in biomolecular structure and function. Probing the chemical diversity of these interactions, we determined that the variable frequency of occurrence in proteins and the ability to synergize with one another are important not only for ab initio structure prediction but also to design proteins with new functionalities. A better understanding of these interactions will promote their utilization in designing and engineering ligands with potential therapeutic value.

The confluence of machine learning and multiscale simulations

Multiscale modeling has a long history of use in structural biology, as computational biologists strive to overcome the time- and length-scale limits of atomistic molecular dynamics. Contemporary machine learning techniques, such as deep learning, have promoted advances in virtually every field of science and engineering and are revitalizing the traditional notions of multiscale modeling. Deep learning has found success in various approaches for distilling information from fine-scale models, such as building surrogate models and guiding the development of coarse-grained potentials. However, perhaps its most powerful use in multiscale modeling is in defining latent spaces that enable efficient exploration of conformational space. This confluence of machine learning and multiscale simulation with modern high-performance computing promises a new era of discovery and innovation in structural biology.

Everything is connected: Graph neural networks

In many ways, graphs are the main modality of data we receive from nature. This is due to the fact that most of the patterns we see, both in natural and artificial systems, are elegantly representable using the language of graph structures. Prominent examples include molecules (represented as graphs of atoms and bonds), social networks and transportation networks. This potential has already been seen by key scientific and industrial groups, with already-impacted application areas including traffic forecasting, drug discovery, social network analysis and recommender systems. Further, some of the most successful domains of application for machine learning in previous years-images, text and speech processing-can be seen as special cases of graph representation learning, and consequently there has been significant exchange of information between these areas. The main aim of this short survey is to enable the reader to assimilate the key concepts in the area, and position graph representation learning in a proper context with related fields.

Before and after AlphaFold2: An overview of protein structure prediction

Three-dimensional protein structure is directly correlated with its function and its determination is critical to understanding biological processes and addressing human health and life science problems in general. Although new protein structures are experimentally obtained over time, there is still a large difference between the number of protein sequences placed in Uniprot and those with resolved tertiary structure. In this context, studies have emerged to predict protein structures by methods based on a template or free modeling. In the last years, different methods have been combined to overcome their individual limitations, until the emergence of AlphaFold2, which demonstrated that predicting protein structure with high accuracy at unprecedented scale is possible. Despite its current impact in the field, AlphaFold2 has limitations. Recently, new methods based on protein language models have promised to revolutionize the protein structural biology allowing the discovery of protein structure and function only from evolutionary patterns present on protein sequence. Even though these methods do not reach AlphaFold2 accuracy, they already covered some of its limitations, being able to predict with high accuracy more than 200 million proteins from metagenomic databases. In this mini-review, we provide an overview of the breakthroughs in protein structure prediction before and after AlphaFold2 emergence.

ProtInteract: A deep learning framework for predicting protein-protein interactions

Proteins mainly perform their functions by interacting with other proteins. Protein-protein interactions underpin various biological activities such as metabolic cycles, signal transduction, and immune response. However, due to the sheer number of proteins, experimental methods for finding interacting and non-interacting protein pairs are time-consuming and costly. We therefore developed the ProtInteract framework to predict protein-protein interaction. ProtInteract comprises two components: first, a novel autoencoder architecture that encodes each protein's primary structure to a lower-dimensional vector while preserving its underlying sequence attributes. This leads to faster training of the second network, a deep convolutional neural network (CNN) that receives encoded proteins and predicts their interaction under three different scenarios. In each scenario, the deep CNN predicts the class of a given encoded protein pair. Each class indicates different ranges of confidence scores corresponding to the probability of whether a predicted interaction occurs or not. The proposed framework features significantly low computational complexity and relatively fast response. The contributions of this work are twofold. First, ProtInteract assimilates the protein's primary structure into a pseudo-time series. Therefore, we leverage the nature of the time series of proteins and their physicochemical properties to encode a protein's amino acid sequence into a lower-dimensional vector space. This approach enables extracting highly informative sequence attributes while reducing computational complexity. Second, the ProtInteract framework utilises this information to identify protein interactions with other proteins based on its amino acid configuration. Our results suggest that the proposed framework performs with high accuracy and efficiency in predicting protein-protein interactions.

Improvement of Modular Protein Display Efficiency in SpyTag-Implemented Norovirus-like Particles

Genetic fusion and chemical conjugation are the most common approaches for displaying a foreign protein on the surface of virus-like particles (VLPs); however, these methods may negatively affect the formation and stability of VLPs. Here, we aimed to develop a modular display platform for protein decoration on norovirus-like particles (NoV-LPs) by combining the NoV-LP scaffold with the SpyTag/SpyCatcher bioconjugation system, as the NoV-LP is an attractive protein nanoparticle to carry foreign proteins for various applications. The SpyTagged-NoV-LPs were prepared by introducing SpyTag peptide into the C-terminus of the norovirus VP1 protein. To increase surface exposure of the SpyTag peptide on the NoV-LPs, two or three repeated extension linkers (EAAAK) were inserted between the SpyTag peptide and VP1 protein. Fluorescence proteins, EGFP and mCherry, were fused to SpyCatcher and employed as SpyTag conjugation partners. These VP1-SpyTag variants and SpyCatcher-fused EGFP and mCherry were separately expressed in silkworm fat bodies and purified. This study reveals that adding an extension linker did not disrupt the VLP formation; instead, it increased the particle size by 4-6 nm. The conjugation efficiency of the VP1-SpyTag variants with the extended linker improved from ∼15-35 to ∼50-63% based on the densitometric analysis, while it was up to 77% based on an optical quantification of EGFP and mCherry. Results indicate that the linker causes the SpyTag peptides to be positioned further away from the C-termini of VP1 and potentially increases the exposure of the SpyTag to the outer surface of the NoV-LPs, allowing more SpyTag/SpyCatcher complex formation on the VLP surface. Our study provides a strategy for enhancing the conjugation efficiency of NoV-LP and demonstrates the platform's utility for developing vaccines or functional nanoparticles.

De novo assembly of a chromosome-level reference genome of the ornamental butterfly Sericinus montelus based on nanopore sequencing and Hi-C analysis

Sericinus montelus (Lepidoptera, Papilionidae, Parnassiinae) is a high-value ornamental swallowtail butterfly species widely distributed in Northern and Central China, Japan, Korea, and Russia. The larval stage of this species feeds exclusively on Aristolochia plants. The Aristolochia species is well known for its high levels of aristolochic acids (AAs), which have been found to be carcinogenic for numerous animals. The swallowtail butterfly is among the few that can feed on these toxic host plants. However, the genetic adaptation of S. montelus to confer new abilities for AA tolerance has not yet been well explored, largely due to the limited genomic resources of this species. This study aimed to present a chromosome-level reference genome for S. montelus using the Oxford Nanopore long-read sequencing, Illumina short-read sequencing, and Hi-C technology. The final assembly was composed of 581.44 Mb with an expected genome size of 619.27 Mb. Further, 99.98% of the bases could be anchored onto 30 chromosomes. The N50 of contigs and scaffolds was 5.74 and 19.12 Mb, respectively. Approximately 48.86% of the assembled genome was suggested to be repeat elements, and 13,720 protein-coding genes were predicted in the current assembly. The phylogenetic analysis indicated that S. montelus diverged from the common ancestor of swallowtails about 58.57-80.46 million years ago. Compared with related species, S. montelus showed a significant expansion of P450 gene family members, and positive selections on eloa, heatr1, and aph1a resulted in the AA tolerance for S. montelus larva. The de novo assembly of a high-quality reference genome for S. montelus provided a fundamental genomic tool for future research on evolution, genome genetics, and toxicology of the swallowtail butterflies.

Energy Profile Bayes and Thompson Optimized Convolutional Neural Network protein structure prediction

In living organisms, proteins are considered as the executants of biological functions. Owing to its pivotal role played in protein folding patterns, comprehension of protein structure is a challenging issue. Moreover, owing to numerous protein sequence exploration in protein data banks and complication of protein structures, experimental methods are found to be inadequate for protein structural class prediction. Hence, it is very much advantageous to design a reliable computational method to predict protein structural classes from protein sequences. In the recent few years there has been an elevated interest in using deep learning to assist protein structure prediction as protein structure prediction models can be utilized to screen a large number of novel sequences. In this regard, we propose a model employing Energy Profile for atom pairs in conjunction with the Legion-Class Bayes function called Energy Profile Legion-Class Bayes Protein Structure Identification model. Followed by this, we use a Thompson Optimized convolutional neural network to extract features between amino acids and then the Thompson Optimized SoftMax function is employed to extract associations between protein sequences for predicting secondary protein structure. The proposed Energy Profile Bayes and Thompson Optimized Convolutional Neural Network (EPB-OCNN) method tested distinct unique protein data and was compared to the state-of-the-art methods, the Template-Based Modeling, Protein Design using Deep Graph Neural Networks, a deep learning-based S-glutathionylation sites prediction tool called a Computational Framework, the Deep Learning and a distance-based protein structure prediction using deep learning. The results obtained when applied with the Biopython tool with respect to protein structure prediction time, protein structure prediction accuracy, specificity, recall, F-measure, and precision, respectively, are measured. The proposed EPB-OCNN method outperformed the state-of-the-art methods, thereby corroborating the objective.

Dephospho-Coenzyme A Kinase Is an Exploitable Drug Target against Plasmodium falciparum: Identification of Selective Inhibitors by High-Throughput Screening of a Large Chemical Compound Library

Malaria is a mosquito-borne fatal infectious disease that affects humans and is caused by Plasmodium parasites, primarily Plasmodium falciparum . Widespread drug resistance compels us to discover novel compounds and alternative drug discovery targets.

AlphaFold, Artificial Intelligence (AI), and Allostery

AlphaFold has burst into our lives. A powerful algorithm that underscores the strength of biological sequence data and artificial intelligence (AI). AlphaFold has appended projects and research directions. The database it has been creating promises an untold number of applications with vast potential impacts that are still difficult to surmise. AI approaches can revolutionize personalized treatments and usher in better-informed clinical trials. They promise to make giant leaps toward reshaping and revamping drug discovery strategies, selecting and prioritizing combinations of drug targets. Here, we briefly overview AI in structural biology, including in molecular dynamics simulations and prediction of microbiota-human protein-protein interactions. We highlight the advancements accomplished by the deep-learning-powered AlphaFold in protein structure prediction and their powerful impact on the life sciences. At the same time, AlphaFold does not resolve the decades-long protein folding challenge, nor does it identify the folding pathways. The models that AlphaFold provides do not capture conformational mechanisms like frustration and allostery, which are rooted in ensembles, and controlled by their dynamic distributions. Allostery and signaling are properties of populations. AlphaFold also does not generate ensembles of intrinsically disordered proteins and regions, instead describing them by their low structural probabilities. Since AlphaFold generates single ranked structures, rather than conformational ensembles, it cannot elucidate the mechanisms of allosteric activating driver hotspot mutations nor of allosteric drug resistance. However, by capturing key features, deep learning techniques can use the single predicted conformation as the basis for generating a diverse ensemble.

Computation-Aided Design of Albumin Affibody-Inserted Antibody Fragment for the Prolonged Serum Half-Life

Single-chain variable fragments (scFvs) have been recognized as promising agents in cancer therapy. However, short serum half-life of scFvs often limits clinical application. Fusion to albumin affibody (ABD) is an effective and convenient half-life extension strategy. Although one terminus of scFv is available for fusion of ABD, it is also frequently used for fusion of useful moieties such as small functional proteins, cytokines, or antibodies. Herein, we investigated the internal linker region for ABD fusion instead of terminal region, which was rarely explored before. We constructed two internally ABD-inserted anti-HER2 4D5scFv (4D5-ABD) variants, which have short (4D5-S-ABD) and long (4D5-L-ABD) linker length respectively. The model structures of these 4D5scFv and 4D5-ABD variants predicted using the deep learning-based protein structure prediction program (AlphaFold2) revealed high similarity to either the original 4D5scFv or the ABD structure, implying that the functionality would be retained. Designed 4D5-ABD variants were expressed in the bacterial expression system and characterized. Both 4D5-ABD variants showed anti-HER2 binding affinity comparable with 4D5scFv. Binding affinity of both 4D5-ABD variants against albumin was also comparable. In a pharmacokinetic study in mice, the 4D5-ABD variants showed a significantly prolonged half-life of 34 h, 114 times longer than that of 4D5scFv. In conclusion, we have developed a versatile scFv platform with enhanced pharmacokinetic profiles with an aid of deep learning-based structure prediction.

Intramolecular covalent bonds in Gram-positive bacterial surface proteins

Gram-positive bacteria experience considerable mechanical perturbation when adhering to host surfaces during colonization and infection. They have evolved various adhesion proteins that are mechanically robust to ensure strong surface adhesion. Recently, it was discovered that these adhesion proteins contain rare, extra intramolecular covalent bonds that stabilize protein structures and participate in surface bonding. These intramolecular covalent bonds include isopeptides, thioesters, and ester bonds, which often form spontaneously without the need for additional enzymes. With the development of single-molecule force spectroscopy techniques, the detailed mechanical roles of these intramolecular covalent bonds have been revealed. In this review, we summarize the recent advances in this area of research, focusing on the link between the mechanical stability and function of these covalent bonds in Gram-positive bacterial surface proteins. We also highlight the potential impact of these discoveries on the development of novel antibiotics and chemical biology tools.

Developing a bioinformatics pipeline for comparative protein classification analysis

BACKGROUND

Protein classification is a task of paramount importance in various fields of biology. Despite the great momentum of modern implementation of protein classification, machine learning techniques such as Random Forest and Neural Network could not always be used for several reasons: data collection, unbalanced classification or labelling of the data.As an alternative, I propose the use of a bioinformatics pipeline to search for and classify information from protein databases. Hence, to evaluate the efficiency and accuracy of the pipeline, I focused on the carotenoid biosynthetic genes and developed a filtering approach to retrieve orthologs clusters in two well-studied plants that belong to the Brassicaceae family: Arabidopsis thaliana and Brassica rapa Pekinensis group. The result obtained has been compared with previous studies on carotenoid biosynthetic genes in B. rapa where phylogenetic analysis was conducted.

RESULTS

The developed bioinformatics pipeline relies on commercial software and multiple databeses including the use of phylogeny, Gene Ontology terms (GOs) and Protein Families (Pfams) at a protein level. Furthermore, the phylogeny is coupled with "population analysis" to evaluate the potential orthologs. All the steps taken together give a final table of potential orthologs. The phylogenetic tree gives a result of 43 putative orthologs conserved in B. rapa Pekinensis group. Different A. thaliana proteins have more than one syntenic ortholog as also shown in a previous finding (Li et al., BMC Genomics 16(1):1-11, 2015).

CONCLUSIONS

This study demonstrates that, when the biological features of proteins of interest are not specific, I can rely on a computational approach in filtering steps for classification purposes. The comparison of the results obtained here for the carotenoid biosynthetic genes with previous research confirmed the accuracy of the developed pipeline which can therefore be applied for filtering different types of datasets.

Inhibition of lysozyme lytic activity by Ivy derived from Photobacterium damselae subsp. piscicida

A pseudotuberculosis pathogen, Photobacterium damselae subsp. piscicida (Pdp), has caused enormous economic damage to yellowtail aquaculture in Japan. The Ivy gene has been discovered in plasmid of Pdp, and it has been proposed that it may help bacteria evade lysozyme-mediated lysis during interaction with an animal host. However, the lysozyme-inhibiting activity of Pdp-derived Ivy (Ivy-Pdp) is unknown, and it is unclear whether it acts as a virulence factor for host biophylaxis. In this study, the inhibitory effect of Ivy-Pdp on lysozyme was evaluated by expressing and purifying the recombinant Ivy-Pdp protein (rIvy-Pdp). The rIvy-Pdp protein inhibited hen egg white lysozyme activity in an rIvy-Pdp-concentration-dependent manner, and its inhibitory effect was similar under different temperature and pH conditions. The serum and skin mucus of the yellowtail (which is the host species of Pdp), Japanese flounder, and Nile tilapia showed bacteriolytic activity. In contrast, the addition of rIvy-Pdp inhibited the lytic activity in the serum of these fish species. In particular, it significantly inhibited lytic activity in the serum and skin mucus of Nile tilapia. On the basis of these results, we suggest that Ivy-Pdp is a temperature- and pH-stable lysozyme inhibitor. Additionally, Ivy-Pdp inhibited the lytic activity of lysozyme, which is involved in host biophylaxis. In summary, we inferred that Ivy-Pdp is an important factor that diminishes the sterilization ability of C-type lysozyme when Pdp infects the host.

Vacuolar Protein-Sorting Receptor MoVps13 Regulates Conidiation and Pathogenicity in Rice Blast Fungus Magnaporthe oryzae

Magnaporthe oryzae (synonym Pyricularia oryzae) is a filamentous fungal pathogen that causes major yield losses in cultivated rice worldwide. However, the mechanisms of infection of M. oryzae are not well characterized. The VPS13 proteins play vital roles in various biological processes in many eukaryotic organisms, including in the organization of actin cytoskeleton, vesicle trafficking, mitochondrial fusion, and phagocytosis. Nevertheless, the function of the Vps13 protein in plant pathogenic fungi has not been explored. Here, we analysed the biological functions of the Vps13 protein in the development and pathogenicity of M.&nbsp;oryzae. Deletion mutants of MoVps13 significantly reduced the conidiation and decreased the rate of fungal infection on hosts. Moreover, the loss of MoVps13 resulted in defective cell wall integrity (CWI) and plasma membrane (PM) homeostasis when treated with chemicals for inducing cell wall stress (200 mg/mL Congo Red or 0.005% SDS) and sphingolipid synthesis inhibitors (2 μM myriocin or 2 μM amphotericin B). This indicated that MoVps13 is also involved in cell wall synthesis and sphingolipid synthesis. Through immunoblotting, autophagic flux detection, co-localization, and chemical drug sensitivity assays, we confirmed the involvement of Movps13 in ER-phagy and the response to ER stress. Additionally, we generated the C-terminal structure of MoVps13 with high accuracy using the alphaflod2 database. Our experimental evidence indicates that MoVps13 is an important virulence factor that regulates the pathogenicity of M.&nbsp;oryzae by controlling CWI, lipid metabolism and the ER-phagy pathway. These results have expanded our knowledge about pathogenic fungi and will help exploration for novel therapeutic strategies against the rice blast fungus.

The AlphaFold Database of Protein Structures: A Biologist's Guide

AlphaFold, the deep learning algorithm developed by DeepMind, recently released the three-dimensional models of the whole human proteome to the scientific community. Here we discuss the advantages, limitations and the still unsolved challenges of the AlphaFold models from the perspective of a biologist, who may not be an expert in structural biology.

Differentiable biology: using deep learning for biophysics-based and data-driven modeling of molecular mechanisms

Deep learning using neural networks relies on a class of machine-learnable models constructed using 'differentiable programs'. These programs can combine mathematical equations specific to a particular domain of natural science with general-purpose, machine-learnable components trained on experimental data. Such programs are having a growing impact on molecular and cellular biology. In this Perspective, we describe an emerging 'differentiable biology' in which phenomena ranging from the small and specific (for example, one experimental assay) to the broad and complex (for example, protein folding) can be modeled effectively and efficiently, often by exploiting knowledge about basic natural phenomena to overcome the limitations of sparse, incomplete and noisy data. By distilling differentiable biology into a small set of conceptual primitives and illustrative vignettes, we show how it can help to address long-standing challenges in integrating multimodal data from diverse experiments across biological scales. This promises to benefit fields as diverse as biophysics and functional genomics.

The Transporter-Mediated Cellular Uptake and Efflux of Pharmaceutical Drugs and Biotechnology Products: How and Why Phospholipid Bilayer Transport Is Negligible in Real Biomembranes

Over the years, my colleagues and I have come to realise that the likelihood of pharmaceutical drugs being able to diffuse through whatever unhindered phospholipid bilayer may exist in intact biological membranes in vivo is vanishingly low. This is because (i) most real biomembranes are mostly protein, not lipid, (ii) unlike purely lipid bilayers that can form transient aqueous channels, the high concentrations of proteins serve to stop such activity, (iii) natural evolution long ago selected against transport methods that just let any undesirable products enter a cell, (iv) transporters have now been identified for all kinds of molecules (even water) that were once thought not to require them, (v) many experiments show a massive variation in the uptake of drugs between different cells, tissues, and organisms, that cannot be explained if lipid bilayer transport is significant or if efflux were the only differentiator, and (vi) many experiments that manipulate the expression level of individual transporters as an independent variable demonstrate their role in drug and nutrient uptake (including in cytotoxicity or adverse drug reactions). This makes such transporters valuable both as a means of targeting drugs (not least anti-infectives) to selected cells or tissues and also as drug targets. The same considerations apply to the exploitation of substrate uptake and product efflux transporters in biotechnology. We are also beginning to recognise that transporters are more promiscuous, and antiporter activity is much more widespread, than had been realised, and that such processes are adaptive (i.e., were selected by natural evolution). The purpose of the present review is to summarise the above, and to rehearse and update readers on recent developments. These developments lead us to retain and indeed to strengthen our contention that for transmembrane pharmaceutical drug transport "phospholipid bilayer transport is negligible".