AlphaFold 2: Why It Works and Its Implications for Understanding the Relationships of Protein Sequence, Structure, and Function

Identification of m6A RNA methylation genes in Oryza sativa and expression profiling in response to different developmental and environmental stimuli

Eukaryotic messenger RNAs (mRNAs) transcend their predominant function of protein encoding by incorporating auxiliary components that ultimately contribute to their processing, transportation, translation, and decay. In doing so, additional layers of modifications are incorporated in mRNAs at post-transcriptional stage. Among them, N6-methyladenosine (m6A) is the most frequently found mRNA modification that plays crucial roles in plant development and stress response. In the overall mechanism of m6A methylation, key proteins classified based on their functions such as writers, readers, and erasers dynamically add, read, and subtract methyl groups respectively to deliver relevant functions in response to external stimuli. In this study, we identified 30 m6A regulatory genes (9 writers, 5 erasers, and 16 readers) in rice that encode 53 proteins (13 writers, 7 erasers, and 33 readers) where segmental duplication was found in one writer and four reader gene pairs. Reproductive cells such as sperm, anther and panicle showed high levels of expression for most of the m6A regulatory genes. Notably, writers like OsMTA, OsMTD, and OsMTC showed varied responses in different stress and infection contexts, with initial upregulation in response to early exposure followed by downregulation later. OsALKBH9A, a noteworthy eraser, displayed varied expression in response to different stresses at different time intervals, but upregulation in certain infections. Reader genes like OsECT5, OsCPSF30-L3, and OsECT8 showed continuous upregulation in exertion of all kinds of stress relevant here. Conversely, other reader genes along with OsECT11 and OsCPSF30-L2 were observed to be consistently downregulated. The apparent correlation between the expression patterns of m6A regulatory genes and stress modulation pathways in this study underscores the need for additional research to unravel their intricate regulatory mechanisms that could ultimately contribute to the substantial development of enhanced stress tolerance in rice through mRNA modification.

In-silico study of protein-protein interactions in wheat blast using docking and molecular dynamics simulation approach

Despite advancements in agricultural research and the introduction of modern biotechnological and farming techniques, food security remains a significant issue. Although the efforts of farmers to meet the demands of a growing population, many plant diseases caused by pathogens, through their effects on cell division and tissue growth, lead to the annual loss of countless food crops. The recently emerged wheat blast fungus Magnaporthe oryzae pathotype Triticum (MoT) poses a significant danger to worldwide wheat cultivation. The fungus is a highly varied lineage of the M. oryzae, responsible for causing rice blast disease. In spite of being a significant challenge to successful wheat production in South America since 1985, the underlying biology of the wheat blast pathogen is still not fully understood. The initial outbreak of the wheat blast in South Asia had a severe impact on wheat production, resulting in a complete loss of yield in affected fields. For the purpose of enhancing disease management, it's vital to acquire a comprehensive comprehension of the infection biology of the fungus and its interaction with wheat plants on molecular levels. Host-pathogen protein interactions (HPIs) have the potential to reveal the pathogens' mechanism for overcoming the host organism. The current study delves into the interactions between the host plant wheat and MoT through protein-protein interactions, molecular docking, and 100 ns molecular dynamic simulations. This research uncovers the structural and functional basis of these proteins, leading to improved plant health and production.Communicated by Ramaswamy H. Sarma.

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.

Conceptual design of a macromolecular diffractometer for the Jülich high brilliance source

In this work, a concept for a neutron diffractometer for high-resolution macromolecular structures has been developed within the Jülich High Brilliance Neutron Source (HBS) project. The SELENE optics are adapted to the requirements of the instrument to achieve a tunable low background neutron beam at mm2 scale sample area. With the optimized guide geometry, a low background neutron beam can be achieved at the small sample area with tunable divergence and size. For the 1 × 1 mm2 sample, a flux of 1.10 × 107 n/s/cm2 for 0.38° divergence is calculated in the 2-4 Å wavelength range, which is about 84.6% of the flux at MaNDi of the high-power spallation source SNS at ORNL. Virtual neutron scattering experiments have been performed to demonstrate the instrument's capabilities for studies of mm scale samples with large unit cells. Results of Vitesse simulations indicate that unit cell sizes of up to 200 Å are possible to be resolved with the proposed instrument.

Protein loop structure prediction by community-based deep learning and its application to antibody CDR H3 loop modeling

As of now, more than 60 years have passed since the first determination of protein structures through crystallography, and a significant portion of protein structures can be predicted by computers. This is due to the groundbreaking enhancement in protein structure prediction achieved through neural network training utilizing extensive sequence and structure data. However, substantial challenges persist in structure prediction due to limited data availability, with antibody structure prediction standing as one such challenge. In this paper, we propose a novel neural network architecture that effectively enables structure prediction by reflecting the inherent combinatorial nature involved in protein structure formation. The core idea of this neural network architecture is not solely to track and generate a single structure but rather to form a community of multiple structures and pursue accurate structure prediction by exchanging information among community members. Applying this concept to antibody CDR H3 loop structure prediction resulted in improved structure sampling. Such an approach could be applied in the structural and functional studies of proteins, particularly in exploring various physiological processes mediated by loops. Moreover, it holds potential in addressing various other types of combinatorial structure prediction and design problems.

Assessing the functional impact of protein binding site definition

Many biomedical applications, such as classification of binding specificities or bioengineering, depend on the accurate definition of protein binding interfaces. Depending on the choice of method used, substantially different sets of residues can be classified as belonging to the interface of a protein. A typical approach used to verify these definitions is to mutate residues and measure the impact of these changes on binding. Besides the lack of exhaustive data, this approach also suffers from the fundamental problem that a mutation introduces an unknown amount of alteration into an interface, which potentially alters the binding characteristics of the interface. In this study we explore the impact of alternative binding site definitions on the ability of a protein to recognize its cognate ligand using a pharmacophore approach, which does not affect the interface. The study also shows that methods for protein binding interface predictions should perform above approximately F-score = 0.7 accuracy level to capture the biological function of a protein.

Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols

Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.

Application of artificial intelligence and machine learning techniques to the analysis of dynamic protein sequences

We apply methods of Artificial Intelligence and Machine Learning to protein dynamic bioinformatics. We rewrite the sequences of a large protein data set, containing both folded and intrinsically disordered molecules, using a representation developed previously, which encodes the intrinsic dynamic properties of the naturally occurring amino acids. We Fourier analyze the resulting sequences. It is demonstrated that classification models built using several different supervised learning methods are able to successfully distinguish folded from intrinsically disordered proteins from sequence alone. It is further shown that the most important sequence property for this discrimination is the sequence mobility, which is the sequence averaged value of the residue-specific average alpha carbon B factor. This is in agreement with previous work, in which we have demonstrated the central role played by the sequence mobility in protein dynamic bioinformatics and biophysics. This finding opens a path to the application of dynamic bioinformatics, in combination with machine learning algorithms, to a range of significant biomedical problems.

Insights into the Dissociation Process and Binding Pattern of the BRCT7/8-PHF8 Complex

DNA topoisomerase 2-binding protein 1 (Topbp1) plays a crucial role in activating the ataxia-telangiectasia mutated and rad3-related (ATR) complex to initiate DNA damage repair responses. For this process to occur, it is necessary for PHF8 to dissociate from Topbp1. Topbp1 binds to the acidic patch sequence (APS) of PHF8 through its C-terminal BRCT7/8 domain, and disrupting this interaction could be a promising strategy for cancer treatment. To investigate the dissociation process and binding pattern of BRCT7/8-PHF8, we employed enhanced sampling techniques, such as steered molecular dynamics (SMD) simulations and accelerated molecular dynamics (aMD) simulations, along with self-organizing maps (SOM) and time-resolved force distribution analysis (TRFDA) methodologies. Our results demonstrate that the dissociation of PHF8 from BRCT7/8 starts from the N-terminus, leading to the unfolding of the N-terminal helix. Additionally, we identified critical residues that play a pivotal role in this dissociation process. These findings provide valuable insights into the disassociation of PHF8 from BRCT7/8, which could potentially guide the development of novel drugs targeting Topbp1 for cancer therapy.

Exploring the fragmentation efficiency of proteins analyzed by MALDI-TOF-TOF tandem mass spectrometry using computational and statistical analyses

Matrix-assisted laser desorption/ionization time-of-flight-time-of-flight (MALDI-TOF-TOF) tandem mass spectrometry (MS/MS) is a rapid technique for identifying intact proteins from unfractionated mixtures by top-down proteomic analysis. MS/MS allows isolation of specific intact protein ions prior to fragmentation, allowing fragment ion attribution to a specific precursor ion. However, the fragmentation efficiency of mature, intact protein ions by MS/MS post-source decay (PSD) varies widely, and the biochemical and structural factors of the protein that contribute to it are poorly understood. With the advent of protein structure prediction algorithms such as Alphafold2, we have wider access to protein structures for which no crystal structure exists. In this work, we use a statistical approach to explore the properties of bacterial proteins that can affect their gas phase dissociation via PSD. We extract various protein properties from Alphafold2 predictions and analyze their effect on fragmentation efficiency. Our results show that the fragmentation efficiency from cleavage of the polypeptide backbone on the C-terminal side of glutamic acid (E) and asparagine (N) residues were nearly equal. In addition, we found that the rearrangement and cleavage on the C-terminal side of aspartic acid (D) residues that result from the aspartic acid effect (AAE) were higher than for E- and N-residues. From residue interaction network analysis, we identified several local centrality measures and discussed their implications regarding the AAE. We also confirmed the selective cleavage of the backbone at D-proline bonds in proteins and further extend it to N-proline bonds. Finally, we note an enhancement of the AAE mechanism when the residue on the C-terminal side of D-, E- and N-residues is glycine. To the best of our knowledge, this is the first report of this phenomenon. Our study demonstrates the value of using statistical analyses of protein sequences and their predicted structures to better understand the fragmentation of the intact protein ions in the gas phase.

AlphaFun: Structural-Alignment-Based Proteome Annotation Reveals why the Functionally Unknown Proteins (uPE1) Are So Understudied

With the rapid expansion of sequencing of genomes, the functional annotation of proteins becomes a bottleneck in understanding proteomes. The Chromosome-centric Human Proteome Project (C-HPP) aims to identify all proteins encoded by the human genome and find functional annotations for them. However, until now there are still 1137 identified human proteins without functional annotation, called uPE1 proteins. Sequence alignment was insufficient to predict their functions, and the crystal structures of most proteins were unavailable. In this study, we demonstrated a new functional annotation strategy, AlphaFun, based on structural alignment using deep-learning-predicted protein structures. Using this strategy, we functionally annotated 99% of the human proteome, including the uPE1 proteins and missing proteins, which have not been identified yet. The accuracy of the functional annotations was validated using the known-function proteins. The uPE1 proteins shared similar functions to the known-function PE1 proteins and tend to express only in very limited tissues. They are evolutionally young genes and thus should conduct functions only in specific tissues and conditions, limiting their occurrence in commonly studied biological models. Such functional annotations provide hints for functional investigations on the uPE1 proteins. This proteome-wide-scale functional annotation strategy is also applicable to any other species.

Investigating the role of functional mutations in leucine binding to Sestrin2 in aging and age-associated degenerative pathologies using structural and molecular simulation approaches

Leucine is the native known ligand of Sestrin2 (Sesn2) and its interaction with Sesn2 is particularly important, as it influences the activity of mTOR in aging and its associated pathologies. It is important to find out how leucine interacts with Sesn2 and how mutations in the binding pocket of leucine affect the binding of leucine. Therefore, this study was committed to investigating the impact of non-synonymous mutations by incorporating a broad spectrum of simulation techniques, from molecular dynamics to free energy calculations. Our study was designed to model the atomic-scale interactions between leucine and mutant forms of Sesn2. Our results demonstrated that the interaction paradigm for the mutants has been altered thus showing a significant decline in the hydrogen bonding network. Moreover, these mutations compromised the dynamic stability by altering the conformational flexibility, sampling time, and leucine-induced structural constraints that consequently caused variation in the binding and structural stability. Molecular dynamics-based flexibility analysis revealed that the regions 217-339 and 371-380 demonstrated a higher fluctuation. Noteworthy, these regions correspond to a linker (217-339) and a loop (371-380) that cover the leucine binding cavity that is critical for the 'latch' mechanism in the N-terminal, which is essential for leucine binding. Further validation of reduced binding and modified internal motions caused by the mutants was obtained through binding free energy calculations, principal components analysis (PCA), and free energy landscape (FEL) analysis. By unraveling the molecular intricacies of Sesn2-leucine interactions and their mutations, we hope to pave the way for innovative strategies to combat the inevitable tide of aging and its associated diseases.Communicated by Ramaswamy H. Sarma.

Designing of a multi-epitopes based vaccine against Haemophilius parainfluenzae and its validation through integrated computational approaches

Haemophilus parainfluenzae is a Gram-negative opportunist pathogen within the mucus of the nose and mouth without significant symptoms and has an ability to cause various infections ranging from ear, eye, and sinus to pneumonia. A concerning development is the increasing resistance of H. parainfluenzae to beta-lactam antibiotics, with the potential to cause dental infections or abscesses. The principal objective of this investigation is to utilize bioinformatics and immuno-informatic methodologies in the development of a candidate multi-epitope Vaccine. The investigation focuses on identifying potential epitopes for both B cells (B lymphocytes) and T cells (helper T lymphocytes and cytotoxic T lymphocytes) based on high non-toxic and non-allergenic characteristics. The selection process involves identifying human leukocyte antigen alleles demonstrating strong associations with recognized antigenic and overlapping epitopes. Notably, the chosen alleles aim to provide coverage for 90% of the global population. Multi-epitope constructs were designed by using suitable linker sequences. To enhance the immunological potential, an adjuvant sequence was incorporated using the EAAAK linker. The final vaccine construct, comprising 344 amino acids, was achieved after the addition of adjuvants and linkers. This multi-epitope Vaccine demonstrates notable antigenicity and possesses favorable physiochemical characteristics. The three-dimensional conformation underwent modeling and refinement, validated through in-silico methods. Additionally, a protein-protein molecular docking analysis was conducted to predict effective binding poses between the multi-epitope Vaccine and the Toll-like receptor 4 protein. The Molecular Dynamics (MD) investigation of the docked TLR4-vaccine complex demonstrated consistent stability over the simulation period, primarily attributed to electrostatic energy. The docked complex displayed minimal deformation and enhanced rigidity in the motion of residues during the dynamic simulation. Furthermore, codon translational optimization and computational cloning was performed to ensure the reliability and proper expression of the multi-Epitope Vaccine. It is crucial to emphasize that despite these computational validations, experimental research in the laboratory is imperative to demonstrate the immunogenicity and protective efficacy of the developed vaccine. This would involve practical assessments to ascertain the real-world effectiveness of the multi-epitope Vaccine.

Characterization of a novel 3-quinuclidinone reductase possessing remarkable thermostability

The 3-quinuclidinone reductase plays an irreplaceable role in the biopreparation of (R)-3-quinuclidinol, an intermediate vital for synthesis of various pharmaceuticals. Thermal robustness is a critical factor for enzymatic synthesis in industrial applications. This study characterized a new 3-quinuclidinone reductase, named SaQR, with significant thermal stability. The SaQR was overexpressed in a GST-fused state, and substrate and cofactor screening were conducted. Additionally, three-dimensional structure prediction using AlphaFold and analysis were performed, along with relevant thermostability tests, and the evaluation of factors influencing enzyme activity. The findings highlight the remarkable thermostability of SaQR, retaining over 90% of its activity after 72 h at 50°C, with an optimal operational temperature of 85°C. SaQR showed typical structural traits of the SDR superfamily, with its cofactor-determining residue being aspartic acid, conferring nicotinamide adenine dinucleotide (NAD(H)) preference. Moreover, K+ and Na+, at a concentration of 400 mM, could significantly enhance the activity, while Mg2+ and Mn2+ only display inhibitory effects within the tested concentration range. The findings of molecular dynamics simulations suggest that high temperatures may disrupt the binding of enzyme to substrate by increasing the flexibility of residues 205-215. In conclusion, this study reports a novel 3-quinuclidinone reductase with remarkable thermostability.

Hypomagnesaemia with varying degrees of extrarenal symptoms as a consequence of heterozygous CNNM2 variants

Variants in the CNNM2 gene are causative for hypomagnesaemia, seizures and intellectual disability, although the phenotypes can be variable. This study aims to understand the genotype-phenotype relationship in affected individuals with CNNM2 variants by phenotypic, functional and structural analysis of new as well as previously reported variants. This results in the identification of seven variants that significantly affect CNNM2-mediated Mg2+ transport. Pathogenicity of these variants is further supported by structural modelling, which predicts CNNM2 structure to be affected by all of them. Strikingly, seizures and intellectual disability are absent in 4 out of 7 cases, indicating these phenotypes are caused either by specific CNNM2 variant only or by additional risk factors. Moreover, in line with sporadic observations from previous reports, CNNM2 variants might be associated with disturbances in parathyroid hormone and Ca2+ homeostasis.

Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design

Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein-protein and protein-ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently. This review highlights the theoretical models incorporating inter-residue interactions in predicting folding and unfolding rates of proteins. Utilizing contact maps to depict inter-residue interactions aids researchers in developing computer models for detecting remote homologs and interface residues within protein-protein complexes which, in turn, enhances our knowledge of the relationship between sequence and structure of proteins. Further, the application of contact maps derived from inter-residue interactions is highlighted in the field of drug discovery. Overall, this review presents an extensive assessment of the significant models that use inter-residue interactions to investigate folding rates, unfolding rates, remote homology, and drug development, providing potential future advancements in constructing efficient computational models in structural biology.

Integrative structural analysis of Pseudomonas phage DEV reveals a genome ejection motor

DEV is an obligatory lytic Pseudomonas phage of the N4-like genus, recently reclassified as Schitoviridae. The DEV genome encodes 91 ORFs, including a 3,398 amino acid virion-associated RNA polymerase. Here, we describe the complete architecture of DEV, determined using a combination of cryo-electron microscopy localized reconstruction, biochemical methods, and genetic knockouts. We built de novo structures of all capsid factors and tail components involved in host attachment. We demonstrate that DEV long tail fibers are essential for infection of Pseudomonas aeruginosa and dispensable for infecting mutants with a truncated lipopolysaccharide devoid of the O-antigen. We identified DEV ejection proteins and, unexpectedly, found that the giant DEV RNA polymerase, the hallmark of the Schitoviridae family, is an ejection protein. We propose that DEV ejection proteins form a genome ejection motor across the host cell envelope and that these structural principles are conserved in all Schitoviridae.

A journey from omics to clinicomics in solid cancers: Success stories and challenges

The word 'cancer' encompasses a heterogenous group of distinct disease types characterized by a spectrum of pathological features, genetic alterations and response to therapies. According to the World Health Organization, cancer is the second leading cause of death worldwide, responsible for one in six deaths and hence imposes a significant burden on global healthcare systems. High-throughput omics technologies combined with advanced imaging tools, have revolutionized our ability to interrogate the molecular landscape of tumors and has provided unprecedented understanding of the disease. Yet, there is a gap between basic research discoveries and their translation into clinically meaningful therapies for improving patient care. To bridge this gap, there is a need to analyse the vast amounts of high dimensional datasets from multi-omics platforms. The integration of multi-omics data with clinical information like patient history, histological examination and imaging has led to the novel concept of clinicomics and may expedite the bench-to-bedside transition in cancer. The journey from omics to clinicomics has gained momentum with development of radiomics which involves extracting quantitative features from medical imaging data with the help of deep learning and artificial intelligence (AI) tools. These features capture detailed information about the tumor's shape, texture, intensity, and spatial distribution. Together, the related fields of multiomics, translational bioinformatics, radiomics and clinicomics may provide evidence-based recommendations tailored to the individual cancer patient's molecular profile and clinical characteristics. In this chapter, we summarize multiomics studies in solid cancers with a specific focus on breast cancer. We also review machine learning and AI based algorithms and their use in cancer diagnosis, subtyping, prognosis and predicting treatment resistance and relapse.

Exploring the Role of Globular Domain Locations on an Intrinsically Disordered Region of p53: A Molecular Dynamics Investigation

The pre-tetramerization loop (PTL) of the human tumor suppressor protein p53 is an intrinsically disordered region (IDR) necessary for the tetramerization process, and its flexibility contributes to the essential conformational changes needed. Although the IDR can be accurately simulated in the traditional manner of molecular dynamics (MD) with the end-to-end distance (EEdist) unhindered, we sought to explore the effects of restraining the EEdist to the values predicted by electron microscopy (EM) and other distances. Simulating the PTL trajectory with a restrained EEdist , we found an increased agreement of nuclear magnetic resonance (NMR) chemical shifts with experiments. Additionally, we observed a plethora of secondary structures and contacts that only appear when the trajectory is restrained. Our findings expand the understanding of the tetramerization of p53 and provide insight into how mutations could make the protein impotent. In particular, our findings demonstrate the importance of restraining the EEdist in studying IDRs and how their conformations change under different conditions. Our results provide a better understanding of the PTL and the conformational dynamics of IDRs in general, which are useful for further studies regarding mutations and their effects on the activity of p53.

Conservation of Hot Spots and Ligand Binding Sites in Protein Models by AlphaFold2

The neural network-based program AlphaFold2 (AF2) provides high accuracy structure prediction for a large fraction of globular proteins. An important question is whether these models are accurate enough for reliably docking small ligands. Several recent papers and the results of CASP15 reveal that local conformational errors reduce the success rates of direct ligand docking. Here, we focus on the ability of the models to conserve the location of binding hot spots, regions on the protein surface that significantly contribute to the binding free energy of the protein-ligand interaction. Clusters of hot spots predict the location and even the druggability of binding sites, and hence are important for computational drug discovery. The hot spots are determined by protein mapping that is based on the distribution of small fragment-sized probes on the protein surface and is less sensitive to local conformation than docking. Mapping models taken from the AlphaFold Protein Structure Database show that identifying binding sites is more reliable than docking, but the success rates are still 5% to 10% lower than based on mapping X-ray structures. The drop in accuracy is particularly large for models of multidomain proteins. However, both the model binding sites and the mapping results can be substantially improved by generating AF2 models for the ligand binding domains of interest rather than the entire proteins and even more if using forced sampling with multiple initial seeds. The mapping of such models tends to reach the accuracy of results obtained by mapping the X-ray structures.

Virtual Screening of Peptide Libraries: The Search for Peptide-Based Therapeutics Using Computational Tools

Over the last few decades, we have witnessed growing interest from both academic and industrial laboratories in peptides as possible therapeutics. Bioactive peptides have a high potential to treat various diseases with specificity and biological safety. Compared to small molecules, peptides represent better candidates as inhibitors (or general modulators) of key protein-protein interactions. In fact, undruggable proteins containing large and smooth surfaces can be more easily targeted with the conformational plasticity of peptides. The discovery of bioactive peptides, working against disease-relevant protein targets, generally requires the high-throughput screening of large libraries, and in silico approaches are highly exploited for their low-cost incidence and efficiency. The present review reports on the potential challenges linked to the employment of peptides as therapeutics and describes computational approaches, mainly structure-based virtual screening (SBVS), to support the identification of novel peptides for therapeutic implementations. Cutting-edge SBVS strategies are reviewed along with examples of applications focused on diverse classes of bioactive peptides (i.e., anticancer, antimicrobial/antiviral peptides, peptides blocking amyloid fiber formation).

Evolution and expression analysis of carotenoid cleavage oxygenase gene family in Chinese mitten crab Eriocheir sinensis

Carotenoid cleavage oxygenase (CCO) plays a pivotal role in various biological activities, including antioxidant and immune functions in animals. This paper investigates the evolution and expression of CCO genes based on three chordates and 27 arthropods. Aquatic animals exhibit a higher abundance of CCO genes. Despite this, research on CCO in crustaceans has been notably limited, with a complete absence of any previous studies on the CCO genes for the Chinese mitten crab (Eriocheir sinensis). In this study, six CCO genes were identified in the E. sinensis genome database. Results reveal that the evolution of the CCO gene family in Crustacea is primarily characterized by purifying selection, with a preference for employing similar codons. EsCCO1 and EsCCO3 were mainly expressed in the epidermal layer, and EsCCO4 was mainly expressed in the hindgut. Meanwhile, EsCCO5 and EsCCO6 were mainly expressed in the hepatopancreas and endometrium. A notable detail that different EsCCO genes demonstrate distinct expression patterns within various tissues of E. sinensis. The findings of this study offer fundamental insights that could serve as a basis for further exploration into the functions and regulatory mechanisms of CCO genes in crustacean species.

Blanket antimicrobial resistance gene database with structural information, BOARDS, provides insights on historical landscape of resistance prevalence and effects of mutations in enzyme structure

Antimicrobial resistance (AMR) in pathogenic bacteria poses a significant threat to public health, yet there is still a need for development in the tools to deeply understand AMR genes based on genetic or structural information. In this study, we present an interactive web database named Blanket Overarching Antimicrobial-Resistance gene Database with Structural information (BOARDS, sbml.unist.ac.kr), a database that comprehensively includes 3,943 reported AMR gene information for 1,997 extended spectrum beta-lactamase (ESBL) and 1,946 other genes as well as a total of 27,395 predicted protein structures. These structures, which include both wild-type AMR genes and their mutants, were derived from 80,094 publicly available whole-genome sequences. In addition, we developed the rapid analysis and detection tool of antimicrobial-resistance (RADAR), a one-stop analysis pipeline to detect AMR genes across whole-genome sequencing (WGSs). By integrating BOARDS and RADAR, the AMR prevalence landscape for eight multi-drug resistant pathogens was reconstructed, leading to unexpected findings such as the pre-existence of the MCR genes before their official reports. Enzymatic structure prediction-based analysis revealed that the occurrence of mutations found in some ESBL genes was found to be closely related to the binding affinities with their antibiotic substrates. Overall, BOARDS can play a significant role in performing in-depth analysis on AMR.IMPORTANCEWhile the increasing antibiotic resistance (AMR) in pathogen has been a burden on public health, effective tools for deep understanding of AMR based on genetic or structural information remain limited. In this study, a blanket overarching antimicrobial-resistance gene database with structure information (BOARDS)-a web-based database that comprehensively collected AMR gene data with predictive protein structural information was constructed. Additionally, we report the development of a RADAR pipeline that can analyze whole-genome sequences as well. BOARDS, which includes sequence and structural information, has shown the historical landscape and prevalence of the AMR genes and can provide insight into single-nucleotide polymorphism effects on antibiotic degrading enzymes within protein structures.

Elucidation of the structural dynamics of mutations in PHB2 protein associated with growth suppression and cancer progression

Prohibitin is a multifunctional protein that plays an important role in numerous cellular processes. Membrane-associated mitochondrial prohibitin complex is made up of two subunits, PHB1 and PHB2 which are ubiquitously expressed and analogous to each other. High levels of prohibitin expression have consequently been found in esophageal cancer, endometrial adenocarcinoma, gastric cancer, hepatocellular carcinoma, breast cancer and bladder cancer. The aim of this study is to analyse two-point mutation PHB2_MT1(I → A) and PHB2_MT2(I → P), their effect on PHB2 protein and its effect on formation of mitochondrial complex. It is a residual level study, based on current experimental validation. To establish the effects of the two-point mutations, computational approaches such as molecular modelling, molecular docking, normal mode simulation, molecular dynamics simulations and MM/GBSA were used. An analysis of the energy dynamics of both unbound and complex proteins was conducted to elucidate how mutations impact the energy distribution of PHB2. Our study confirmed that the two mutations decreased the overall stability of PHB2. This was evidenced by heightened atomic fluctuations within the mutated region, accompanied by elevated deviations observed in RMSD and Rg values. Furthermore, these mutations were correlated with a decline in the organization of secondary structural elements. The mutations in PHB2_MT1 and PHB2_MT2 resulted in formation a less stable prohibitin complex. Thus, PHB1 and PHB2 may act as molecular target or novel biomarkers for therapeutic intervention in numerous forms of malignancies.

Protein Multiple Conformation Prediction Using Multi-Objective Evolution Algorithm

The breakthrough of AlphaFold2 and the publication of AlphaFold DB represent a significant advance in the field of predicting static protein structures. However, AlphaFold2 models tend to represent a single static structure, and multiple-conformation prediction remains a challenge. In this work, we proposed a method named MultiSFold, which uses a distance-based multi-objective evolutionary algorithm to predict multiple conformations. To begin, multiple energy landscapes are constructed using different competing constraints generated by deep learning. Subsequently, an iterative modal exploration and exploitation strategy is designed to sample conformations, incorporating multi-objective optimization, geometric optimization and structural similarity clustering. Finally, the final population is generated using a loop-specific sampling strategy to adjust the spatial orientations. MultiSFold was evaluated against state-of-the-art methods using a benchmark set containing 80 protein targets, each characterized by two representative conformational states. Based on the proposed metric, MultiSFold achieves a remarkable success ratio of 56.25% in predicting multiple conformations, while AlphaFold2 only achieves 10.00%, which may indicate that conformational sampling combined with knowledge gained through deep learning has the potential to generate conformations spanning the range between different conformational states. In addition, MultiSFold was tested on 244 human proteins with low structural accuracy in AlphaFold DB to test whether it could further improve the accuracy of static structures. The experimental results demonstrate the performance of MultiSFold, with a TM-score better than that of AlphaFold2 by 2.97% and RoseTTAFold by 7.72%. The online server is at http://zhanglab-bioinf.com/MultiSFold .

Structural and molecular investigation of the impact of S30L and D88N substitutions in G9R protein on coupling with E4R from Monkeypox virus (MPXV)

Understanding the pathogenesis mechanism of the Monkeypox virus (MPXV) is essential to guide therapeutic development against the Monkeypox virus. In the current study, we investigated the impact of the only two reported substitutions, S30L, D88N, and S30L-D88N on the G9R of the replication complex in 2022 with E4R using structural modeling, simulation, and free energy calculation methods. From the molecular docking and dissociation constant (KD) results, it was observed that the binding affinity did not increase in the mutants, but the interaction paradigm was altered by these substitutions. Molecular simulation data revealed that these mutations are responsible for destabilization, changes in protein packing, and internal residue fluctuations, which can cause functional variance. Additionally, hydrogen bonding analysis revealed that the estimated number of hydrogen bonds are almost equal among the wild-type G9R and each mutant. The total binding free energy for the wild-type G9R with E4R was -85.00 kcal/mol while for the mutants the TBE was -42.75 kcal/mol, -43.68 kcal/mol, and -48.65 kcal/mol respectively. This shows that there is no direct impact of these two reported mutations on the binding with E4R, or it may affect the whole replication complex or any other mechanism involved in pathogenesis. To explore these variations further, we conducted PCA and FEL analyses. Based on our findings, we speculate that within the context of interaction with E4R, the mutations in the G9R protein might be benign, potentially leading to functional diversity associated with other proteins.Communicated by Ramaswamy H. Sarma.

Animal Models of FUS-Proteinopathy: A Systematic Review

Mutations that disrupt the function of the DNA/RNA-binding protein FUS could cause amyotrophic lateral sclerosis (ALS) and other neurodegenerative diseases. One of the key features in ALS pathogenesis is the formation of insoluble protein aggregates containing aberrant isoforms of the FUS protein in the cytoplasm of upper and lower motor neurons. Reproduction of human pathology in animal models is the main tool for studying FUS-associated pathology and searching for potential therapeutic agents for ALS treatment. In this review, we provide a systematic analysis of the role of FUS protein in ALS pathogenesis and an overview of the results of modelling FUS-proteinopathy in animals.

Functional prediction of proteins from the human gut archaeome

The human gastrointestinal tract contains diverse microbial communities, including archaea. Among them, Methanobrevibacter smithii represents a highly active and clinically relevant methanogenic archaeon, being involved in gastrointestinal disorders, such as inflammatory bowel disease and obesity. Herein, we present an integrated approach using sequence and structure information to improve the annotation of M. smithii proteins using advanced protein structure prediction and annotation tools, such as AlphaFold2, trRosetta, ProFunc, and DeepFri. Of an initial set of 873 481 archaeal proteins, we found 707 754 proteins exclusively present in the human gut. Having analysed archaeal proteins together with 87 282 994 bacterial proteins, we identified unique archaeal proteins and archaeal-bacterial homologs. We then predicted and characterized functional domains and structures of 73 unique and homologous archaeal protein clusters linked the human gut and M. smithii. We refined annotations based on the predicted structures, extending existing sequence similarity-based annotations. We identified gut-specific archaeal proteins that may be involved in defense mechanisms, virulence, adhesion, and the degradation of toxic substances. Interestingly, we identified potential glycosyltransferases that could be associated with N-linked and O-glycosylation. Additionally, we found preliminary evidence for interdomain horizontal gene transfer between Clostridia species and M. smithii, which includes sporulation Stage V proteins AE and AD. Our study broadens the understanding of archaeal biology, particularly M. smithii, and highlights the importance of considering both sequence and structure for the prediction of protein function.

Use of Localized Reconstruction to Visualize the Shigella Phage Sf6 Tail Apparatus

Cryogenic electron microscopy (cryo-EM) single-particle analysis has revolutionized the structural analysis of icosahedral viruses, including tailed bacteriophages. In recent years, localized (or focused) reconstruction has emerged as a powerful data analysis method to capture symmetry mismatches and resolve asymmetric features in icosahedral viruses. Here, we describe the methods used to reconstruct the 2.65-MDa tail apparatus of the Shigella phage Sf6, a representative member of the Podoviridae superfamily.

Comparative Analysis of Inhibitor Binding to Peroxiredoxins from Candidatus Liberibacter asiaticus and Its Host Citrus sinensis

The peroxiredoxins (Prxs), potential drug targets, constitute an important class of antioxidant enzymes present in both pathogen and their host. The comparative binding potential of inhibitors to Prxs from pathogen and host could be an important step in drug development against pathogens. Huanglongbing (HLB) is a most devastating disease of citrus caused by Candidatus Liberibacter asiaticus (CLa). In this study, the binding of conoidin-A (conoidin) and celastrol inhibitor molecules to peroxiredoxin of bacterioferritin comigratory protein family from CLa (CLaBCP) and its host plant peroxiredoxin from Citrus sinensis (CsPrx) was assessed. The CLaBCP has a lower specific activity than CsPrx and is efficiently inhibited by conoidin and celastrol molecules. The biophysical studies showed conformational changes and significant thermal stability of CLaBCP in the presence of inhibitor molecules as compared to CsPrx. The surface plasmon resonance (SPR) studies revealed that the conoidin and celastrol inhibitor molecules have a strong binding affinity (KD) with CLaBCP at 33.0 µM, and 18.5 µM as compared to CsPrx at 52.0 µM and 61.6 µM, respectively. The docked complexes of inhibitor molecules showed more structural stability of CLaBCP as compared to CsPrx during the run of molecular dynamics-based simulations for 100 ns. The present study suggests that the conoidin and celastrol molecules can be exploited as potential inhibitor molecules against the CLa to manage the HLB disease.

Evaluation of Myocilin Variant Protein Structures Modeled by AlphaFold2

Deep neural network-based programs can be applied to protein structure modeling by inputting amino acid sequences. Here, we aimed to evaluate the AlphaFold2-modeled myocilin wild-type and variant protein structures and compare to the experimentally determined protein structures. Molecular dynamic and ligand binding properties of the experimentally determined and AlphaFold2-modeled protein structures were also analyzed. AlphaFold2-modeled myocilin variant protein structures showed high similarities in overall structure to the experimentally determined mutant protein structures, but the orientations and geometries of amino acid side chains were slightly different. The olfactomedin-like domain of the modeled missense variant protein structures showed fewer folding changes than the nonsense variant when compared to the predicted wild-type protein structure. Differences were also observed in molecular dynamics and ligand binding sites between the AlphaFold2-modeled and experimentally determined structures as well as between the wild-type and variant structures. In summary, the folding of the AlphaFold2-modeled MYOC variant protein structures could be similar to that determined by the experiments but with differences in amino acid side chain orientations and geometries. Careful comparisons with experimentally determined structures are needed before the applications of the in silico modeled variant protein structures.

Deep learning in bioinformatics

Deep learning is a powerful machine learning technique that can learn from large amounts of data using multiple layers of artificial neural networks. This paper reviews some applications of deep learning in bioinformatics, a field that deals with analyzing and interpreting biological data. We first introduce the basic concepts of deep learning and then survey the recent advances and challenges of applying deep learning to various bioinformatics problems, such as genome sequencing, gene expression analysis, protein structure prediction, drug discovery, and disease diagnosis. We also discuss future directions and opportunities for deep learning in bioinformatics. We aim to provide an overview of deep learning so that bioinformaticians applying deep learning models can consider all critical technical and ethical aspects. Thus, our target audience is biomedical informatics researchers who use deep learning models for inference. This review will inspire more bioinformatics researchers to adopt deep-learning methods for their research questions while considering fairness, potential biases, explainability, and accountability.

TATDN2 resolution of R-loops is required for survival of BRCA1-mutant cancer cells

BRCA1-deficient cells have increased IRE1 RNase, which degrades multiple microRNAs. Reconstituting expression of one of these, miR-4638-5p, resulted in synthetic lethality in BRCA1-deficient cancer cells. We found that miR-4638-5p represses expression of TATDN2, a poorly characterized member of the TATD nuclease family. We discovered that human TATDN2 has RNA 3' exonuclease and endonuclease activity on double-stranded hairpin RNA structures. Given the cleavage of hairpin RNA by TATDN2, and that BRCA1-deficient cells have difficulty resolving R-loops, we tested whether TATDN2 could resolve R-loops. Using in vitro biochemical reconstitution assays, we found TATDN2 bound to R-loops and degraded the RNA strand but not DNA of multiple forms of R-loops in vitro in a Mg2+-dependent manner. Mutations in amino acids E593 and E705 predicted by Alphafold-2 to chelate an essential Mg2+ cation completely abrogated this R-loop resolution activity. Depleting TATDN2 increased cellular R-loops, DNA damage and chromosomal instability. Loss of TATDN2 resulted in poor replication fork progression in the presence of increased R-loops. Significantly, we found that TATDN2 is essential for survival of BRCA1-deficient cancer cells, but much less so for cognate BRCA1-repleted cancer cells. Thus, we propose that TATDN2 is a novel target for therapy of BRCA1-deficient cancers.

Multi-domain and complex protein structure prediction using inter-domain interactions from deep learning

Accurately capturing domain-domain interactions is key to understanding protein function and designing structure-based drugs. Although AlphaFold2 has made a breakthrough on single domain, it should be noted that the structure modeling for multi-domain protein and complex remains a challenge. In this study, we developed a multi-domain and complex structure assembly protocol, named DeepAssembly, based on domain segmentation and single domain modeling algorithms. Firstly, DeepAssembly uses a population-based evolutionary algorithm to assemble multi-domain proteins by inter-domain interactions inferred from a developed deep learning network. Secondly, protein complexes are assembled by means of domains rather than chains using DeepAssembly. Experimental results show that on 219 multi-domain proteins, the average inter-domain distance precision by DeepAssembly is 22.7% higher than that of AlphaFold2. Moreover, DeepAssembly improves accuracy by 13.1% for 164 multi-domain structures with low confidence deposited in AlphaFold database. We apply DeepAssembly for the prediction of 247 heterodimers. We find that DeepAssembly successfully predicts the interface (DockQ ≥ 0.23) for 32.4% of the dimers, suggesting a lighter way to assemble complex structures by treating domains as assembly units and using inter-domain interactions learned from monomer structures.

Quality Assessment of Selected Protein Structures Derived from Homology Modeling and AlphaFold

With technology advancing, many prediction algorithms have been developed to facilitate the modeling of inherently dynamic and flexible macromolecules such as proteins. Improvements in the prediction of protein structures have attracted a great deal of attention due to the advantages they offer, e.g., in drug design. While trusted experimental methods, such as X-ray crystallography, NMR spectroscopy, and electron microscopy, are preferred structure analysis techniques, in silico approaches are also being widely used. Two computational methods, which are on opposite ends of the spectrum with respect to their modus operandi, i.e., homology modeling and AlphaFold, have been established to provide high-quality structures. Here, a comparative study of the quality of structures either predicted by homology modeling or by AlphaFold is presented based on the characteristics determined by experimental studies using structure validation servers to fulfill the purpose. Although AlphaFold is able to predict high-quality structures, high-confidence parts are sometimes observed to be in disagreement with experimental data. On the other hand, while the structures obtained from homology modeling are successful in incorporating all aspects of the experimental structure used as a template, this method may struggle to accurately model a structure in the absence of a suitable template. In general, although both methods produce high-quality models, the criteria by which they are superior to each other are different and thus discussed in detail.

A Proposal for a Consolidated Structural Model of the CagY Protein of Helicobacter pylori

CagY is the largest and most complex protein from Helicobacter pylori's (Hp) type IV secretion system (T4SS), playing a critical role in the modulation of gastric inflammation and risk for gastric cancer. CagY spans from the inner to the outer membrane, forming a channel through which Hp molecules are injected into human gastric cells. Yet, a tridimensional structure has been reported for only short segments of the protein. This intricate protein was modeled using different approaches, including homology modeling, ab initio, and deep learning techniques. The challengingly long middle repeat region (MRR) was modeled using deep learning and optimized using equilibrium molecular dynamics. The previously modeled segments were assembled into a 1595 aa chain and a 14-chain CagY multimer structure was assembled by structural alignment. The final structure correlated with published structures and allowed to show how the multimer may form the T4SS channel through which CagA and other molecules are translocated to gastric cells. The model confirmed that MRR, the most polymorphic and complex region of CagY, presents numerous cysteine residues forming disulfide bonds that stabilize the protein and suggest this domain may function as a contractile region playing an essential role in the modulating activity of CagY on tissue inflammation.

Novel Covalent Modifier-Induced Local Conformational Changes within the Intrinsically Disordered Region of the Androgen Receptor

Intrinsically disordered regions (IDRs) of transcription factors play an important biological role in liquid condensate formation and gene regulation. It is thus desirable to investigate the druggability of IDRs and how small-molecule binders can alter their conformational stability. For the androgen receptor (AR), certain covalent ligands induce important changes, such as the neutralization of the condensate. To understand the specificity of ligand-IDR interaction and potential implications for the mechanism of neutralizing liquid-liquid phase separation (LLPS), we modeled and performed computer simulations of ligand-bound peptide segments obtained from the human AR. We analyzed how different covalent ligands affect local secondary structure, protein contact map, and protein-ligand contacts for these protein systems. We find that effective neutralizers make specific interactions (such as those between cyanopyrazole and tryptophan) that alter the helical propensity of the peptide segments. These findings on the mechanism of action can be useful for designing molecules that influence IDR structure and condensate of the AR in the future.

A project-oriented biochemistry laboratory for protein engineering and structure-function using small laccase enzyme from Streptomyces coelicolor

An understanding of structure-function relationships in proteins is essential for modern biochemical studies. The integration of common freely accessible bioinformatics tools available online with the knowledge of protein-engineering tools provide a fundamental understanding of the application of protein structure-function for biochemical research. In order for students to apply their prior knowledge of recombinant protein technology into the understanding of protein structure-function relationships, we developed a semester-long project-oriented biochemistry laboratory experience that is the second laboratory course of a series. For easier integration of knowledge and application, we organized this course into four sequential modules: protein structure visualization/modification, mutagenesis target identification, site-directed mutagenesis, and mutant protein expression, purification, and characterization. These tasks were performed on the protein small laccase (SLAC) that was cloned and characterized by students in the previous semester during the first biochemistry laboratory course of the series. This goal-oriented project-based approach helped students apply their prior knowledge to newly introduced techniques to understand protein structure-function relationships in this research-like laboratory setting. A student assessment before and after the course demonstrated an overall increase in learning and enthusiasm for this topic.

Protein quality assessment with a loss function designed for high-quality decoys

Motivation: The prediction of a protein 3D structure is essential for understanding protein function, drug discovery, and disease mechanisms; with the advent of methods like AlphaFold that are capable of producing very high-quality decoys, ensuring the quality of those decoys can provide further confidence in the accuracy of their predictions. Results: In this work, we describe Qϵ, a graph convolutional network (GCN) that utilizes a minimal set of atom and residue features as inputs to predict the global distance test total score (GDTTS) and local distance difference test (lDDT) score of a decoy. To improve the model's performance, we introduce a novel loss function based on the ϵ-insensitive loss function used for SVM regression. This loss function is specifically designed for evaluating the characteristics of the quality assessment problem and provides predictions with improved accuracy over standard loss functions used for this task. Despite using only a minimal set of features, it matches the performance of recent state-of-the-art methods like DeepUMQA. Availability: The code for Qϵ is available at https://github.com/soumyadip1997/qepsilon.

Genome Sequencing of Consanguineous Family Implicates Ubiquitin-Specific Protease 53 (USP53) Variant in Psychosis/Schizophrenia: Wild-Type Expression in Murine Hippocampal CA 1-3 and Granular Dentate with AMPA Synapse Interactions

Psychosis is a severe mental disorder characterized by abnormal thoughts and perceptions (e.g., hallucinations) occurring quintessentially in schizophrenia and in several other neuropsychiatric disorders. Schizophrenia is widely considered as a neurodevelopmental disorder that onsets during teenage/early adulthood. A multiplex consanguineous Pakistani family was afflicted with severe psychosis and apparent autosomal recessive transmission. The first-cousin parents and five children were healthy, whereas two teenage daughters were severely affected. Structured interviews confirmed the diagnosis of DSM-V schizophrenia. Probands and father underwent next-generation sequencing. All available relatives were subjected to confirmatory Sanger sequencing. Homozygosity mapping and directed a priori filtering identified only one rare variant [MAF < 5(10)-5] at a residue conserved across vertebrates. The variant was a non-catalytic deubiquitinase, USP53 (p.Cys228Arg), predicted in silico as damaging. Genome sequencing did not identify any other potentially pathogenic single nucleotide variant or structural variant. Since the literature on USP53 lacked relevance to mental illness or CNS expression, studies were conducted which revealed USP53 localization in regions of the hippocampus (CA 1-3) and granular dentate. The staining pattern was like that seen with GRIA2/GluA2 and GRIP2 antibodies. All three proteins coimmunoprecipitated. These findings support the glutamate hypothesis of schizophrenia as part of the AMPA-R interactome. If confirmed, USP53 appears to be one of the few Mendelian variants potentially causal to a common-appearing mental disorder that is a rare genetic form of schizophrenia.

Biophysical investigation of interactions between SARS-CoV-2 spike protein and neuropilin-1

Recent studies have suggested that neuropilin-1 (NRP1) may serve as a potential receptor in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. However, the biophysical characteristics of interactions between NRP1 and SARS-CoV-2 remain unclear. In this study, we examined the interactions between NRP1 and various SARS-CoV-2 spike (S) fragments, including the receptor-binding domain (RBD) and the S protein trimer in a soluble form or expressed on pseudovirions, using atomic force microscopy and structural modeling. Our measurements shows that NRP1 interacts with the RBD and trimer at a higher binding frequency (BF) compared to ACE2. This NRP1-RBD interaction has also been predicted and simulated via AlphaFold2 and molecular dynamics simulations, and the results indicate that their binding patterns are very similar to RBD-ACE2 interactions. Additionally, under similar loading rates, the most probable unbinding forces between NRP1 and S trimer (both soluble form and on pseudovirions) are larger than the forces between NRP1 and RBD and between trimer and ACE2. Further analysis indicates that NRP1 has a stronger binding affinity to the SARS-CoV-2 S trimer with a dissociation rate of 0.87 s-1 , four times lower than the dissociation rate of 3.65 s-1 between NRP1 and RBD. Moreover, additional experiments show that RBD-neutralizing antibodies can significantly reduce the BF for both ACE2 and NRP1. Together, the study suggests that NRP1 can be an alternative receptor for SARS-CoV-2 attachment to human cells, and the neutralizing antibodies targeting SARS-CoV-2 RBD can reduce the binding between SARS-CoV-2 and NRP1.

Starting at Go: Protein structure prediction succumbs to machine learning

This year's Lasker Basic Science Award recognizes the invention of AlphaFold, a revolutionary advance in the history of protein research which for the first time offers the practical ability to accurately predict the three-dimensional arrangement of amino acids in the vast majority of proteins on a genomic scale on the basis of sequence alone [J. Jumper et al., Nature 596, 583-589 (2021) and K. Tunyasuvunakool et al., Nature 596, 590-596 (2021)]. This extraordinary achievement by Demis Hassabis and John Jumper and their coworkers at Google's DeepMind and other collaborators was built on decades of experimental protein structure determination (structural biology) as well as the gradual development of multiple strategies incorporating biologically inspired statistical approaches. But when Jumper and Hassabis added a brew of innovative neural network-based machine learning approaches to the mix, the results were explosive. Realizing the half-century-old dream of predicting protein structure has already accelerated the pace and creativity of many areas of Chemistry, Biology, and Medicine.

Comparison, Analysis, and Molecular Dynamics Simulations of Structures of a Viral Protein Modeled Using Various Computational Tools

The structural analysis of proteins is a major domain of biomedical research. Such analysis requires resolved three-dimensional structures of proteins. Advancements in computer technology have led to progress in biomedical research. In silico prediction and modeling approaches have facilitated the construction of protein structures, with or without structural templates. In this study, we used three neural network-based de novo modeling approaches-AlphaFold2 (AF2), Robetta-RoseTTAFold (Robetta), and transform-restrained Rosetta (trRosetta)-and two template-based tools-the Molecular Operating Environment (MOE) and iterative threading assembly refinement (I-TASSER)-to construct the structure of a viral capsid protein, hepatitis C virus core protein (HCVcp), whose structure have not been fully resolved by laboratory techniques. Templates with sufficient sequence identity for the homology modeling of complete HCVcp are currently unavailable. Therefore, we performed domain-based homology modeling for MOE simulations. The templates for each domain were obtained through sequence-based searches on NCBI and the Protein Data Bank. Then, the modeled domains were assembled to construct the complete structure of HCVcp. The full-length structure and two truncated forms modeled using various computational tools were compared. Molecular dynamics (MD) simulations were performed to refine the structures. The root mean square deviation of backbone atoms, root mean square fluctuation of Cα atoms, and radius of gyration were calculated to monitor structural changes and convergence in the simulations. The model quality was evaluated through ERRAT and phi-psi plot analysis. In terms of the initial prediction for protein modeling, Robetta and trRosetta outperformed AF2. Regarding template-based tools, MOE outperformed I-TASSER. MD simulations resulted in compactly folded protein structures, which were of good quality and theoretically accurate. Thus, the predicted structures of certain proteins must be refined to obtain reliable structural models. MD simulation is a promising tool for this purpose.

Domain loss enabled evolution of novel functions in the snake three-finger toxin gene superfamily

Three-finger toxins (3FTXs) are a functionally diverse family of toxins, apparently unique to venoms of caenophidian snakes. Although the ancestral function of 3FTXs is antagonism of nicotinic acetylcholine receptors, redundancy conferred by the accumulation of duplicate genes has facilitated extensive neofunctionalization, such that derived members of the family interact with a range of targets. 3FTXs are members of the LY6/UPAR family, but their non-toxin ancestor remains unknown. Combining traditional phylogenetic approaches, manual synteny analysis, and machine learning techniques (including AlphaFold2 and ProtT5), we have reconstructed a detailed evolutionary history of 3FTXs. We identify their immediate ancestor as a non-secretory LY6, unique to squamate reptiles, and propose that changes in molecular ecology resulting from loss of a membrane-anchoring domain and changes in gene expression, paved the way for the evolution of one of the most important families of snake toxins.

Discovery and Characterization of a Myxobacterial Lanthipeptide with Unique Biosynthetic Features and Anti-inflammatory Activity

The genomes of myxobacteria harbor a variety of biosynthetic gene clusters encoding numerous secondary metabolites, including ribosomally synthesized and post-translationally modified peptides (RiPPs) with diverse chemical structures and biological activities. However, the biosynthetic potential of RiPPs from myxobacteria remains barely explored. Herein, we report a novel myxobacteria lanthipeptide myxococin identified from Myxococcus fulvus. Myxococins represent the first example of lanthipeptides, of which the characteristic multiple thioether rings are installed by employing a Class II lanthipeptide synthetase MfuM and a Class I lanthipeptide cyclase MfuC in a cascaded way. Unprecedentedly, we biochemically characterized the first M61 family aminopeptidase MfuP involved in RiPP biosynthesis, demonstrating that MfuP showed the activity of an endopeptidase activity. MfuP is leader-independent but strictly selective for the multibridge structure of myxococin A and responsible for unwrapping two rings via amide bond hydrolysis, yielding myxococin B. Furthermore, the X-ray crystal structure of MfuP and structural analysis, including active-site mutations, are reported. Finally, myxococins are evaluated to exhibit anti-inflammatory activity in lipopolysaccharide-induced macrophages without detectable cytotoxicity.

More than just pattern recognition: Prediction of uncommon protein structure features by AI methods

The CASP14 experiment demonstrated the extraordinary structure modeling capabilities of artificial intelligence (AI) methods. That result has ignited a fierce debate about what these methods are actually doing. One of the criticisms has been that the AI does not have any sense of the underlying physics but is merely performing pattern recognition. Here, we address that issue by analyzing the extent to which the methods identify rare structural motifs. The rationale underlying the approach is that a pattern recognition machine tends to choose the more frequently occurring motifs, whereas some sense of subtle energetic factors is required to choose infrequently occurring ones. To reduce the possibility of bias from related experimental structures and to minimize the effect of experimental errors, we examined only CASP14 target protein crystal structures determined to a resolution limit better than 2 Å, which lacked significant amino acid sequence homology to proteins of known structure. In those experimental structures and in the corresponding models, we track cis peptides, π-helices, 310-helices, and other small 3D motifs that occur in the PDB database at a frequency of lower than 1% of total amino acid residues. The best-performing AI method, AlphaFold2, captured these uncommon structural elements exquisitely well. All discrepancies appeared to be a consequence of crystal environment effects. We propose that the neural network learned a protein structure potential of mean force, enabling it to correctly identify situations where unusual structural features represent the lowest local free energy because of subtle influences from the atomic environment.

High-resolution cryo-EM structure of the Pseudomonas bacteriophage E217

E217 is a Pseudomonas phage used in an experimental cocktail to eradicate cystic fibrosis-associated Pseudomonas aeruginosa. Here, we describe the structure of the whole E217 virion before and after DNA ejection at 3.1 Å and 4.5 Å resolution, respectively, determined using cryogenic electron microscopy (cryo-EM). We identify and build de novo structures for 19 unique E217 gene products, resolve the tail genome-ejection machine in both extended and contracted states, and decipher the complete architecture of the baseplate formed by 66 polypeptide chains. We also determine that E217 recognizes the host O-antigen as a receptor, and we resolve the N-terminal portion of the O-antigen-binding tail fiber. We propose that E217 design principles presented in this paper are conserved across PB1-like Myoviridae phages of the Pbunavirus genus that encode a ~1.4 MDa baseplate, dramatically smaller than the coliphage T4.

Phylogenetic and Genomic Characterization of Whole Genome Sequences of Ocular Herpes Simplex Virus Type 1 Isolates Identifies Possible Virulence Determinants in Humans

Purpose

There are limited data on the prevalence and genetic diversity of herpes simplex virus type 1 (HSV-1) virulence genes in ocular isolates. Here, we sequenced 36 HSV-1 ocular isolates, collected by the Bascom Palmer Eye Institute, a university-based eye hospital, from three different ocular anatomical sites (conjunctiva, cornea, and eyelid) and carried out a genomic and phylogenetic analyses.

Methods

The PacBio Sequel II long read platform was used for genome sequencing. Phylogenetic analysis and genomic analysis were performed to help better understand genetic variability among common virulence genes in ocular herpetic disease.

Results

A phylogenetic network generated using the genome sequences of the 36 Bascom Palmer ocular isolates, plus 174 additional strains showed that ocular isolates do not group together phylogenetically. Analysis of the thymidine kinase and DNA polymerase protein sequences from the Bascom Palmer isolates showed multiple novel single nucleotide polymorphisms, but only one, BP-K14 encoded a known thymidine kinase acyclovir resistance mutation. An analysis of the multiple sequence alignment comprising the 51 total ocular isolates versus 159 nonocular strains detected several possible single nucleotide polymorphisms in HSV-1 genes that were found significantly more often in the ocular isolates. These genes included UL6, gM, VP19c, VHS, gC, VP11/12, and gG.

Conclusions

There does not seem to be a specific genetic feature of viruses causing ocular infection. The identification of novel and common recurrent polymorphisms may help to understand the drivers of herpetic pathogenicity and specific factors that may influence the virulence of ocular disease.

Omics and systems view of innate immune pathways

Multiomics approaches to studying systems biology are very powerful techniques that can elucidate changes in the genomic, transcriptomic, proteomic, and metabolomic levels within a cell type in response to an infection. These approaches are valuable for understanding the mechanisms behind disease pathogenesis and how the immune system responds to being challenged. With the emergence of the COVID-19 pandemic, the importance and utility of these tools have become evident in garnering a better understanding of the systems biology within the innate and adaptive immune response and for developing treatments and preventative measures for new and emerging pathogens that pose a threat to human health. In this review, we focus on state-of-the-art omics technologies within the scope of innate immunity.