Protein structure prediction and structural genomics

An easy-to-use three-dimensional protein-structure-prediction online platform "DPL3D" based on deep learning algorithms

The change in the three-dimensional (3D) structure of a protein can affect its own function or interaction with other protein(s), which may lead to disease(s). Gene mutations, especially missense mutations, are the main cause of changes in protein structure. Due to the lack of protein crystal structure data, about three-quarters of human mutant proteins cannot be predicted or accurately predicted, and the pathogenicity of missense mutations can only be indirectly evaluated by evolutionary conservation. Recently, many computational methods have been developed to predict protein 3D structures with accuracy comparable to experiments. This progress enables the information of structural biology to be further utilized by clinicians. Thus, we developed a user-friendly platform named DPL3D (http://nsbio.tech:3000) which can predict and visualize the 3D structure of mutant proteins. The crystal structure and other information of proteins were downloaded together with the software including AlphaFold 2, RoseTTAFold, RoseTTAFold All-Atom, and trRosettaX-Single. We implemented a query module for 210,180 molecular structures, including 52,248 human proteins. Visualization of protein two-dimensional (2D) and 3D structure prediction can be generated via LiteMol automatically or manually and interactively. This platform will allow users to easily and quickly retrieve large-scale structural information for biological discovery.

A comprehensive review on structural insights through molecular visualization: tools, applications, and limitations

CONTEXT

Biomolecules serve as intrinsic repositories of information related to function, binding interactions, molecular motion, and structural conformations. With the rapid accumulation of structural data from fields such as structural biology and cheminformatics, the ability to visualize biomolecular architecture has become essential for researchers in biology, pharmacology, and related disciplines. Molecular visualization represents a foundational step in accessing and interpreting this data, enabling its application in diverse scientific and therapeutic contexts. Recent advancements in computational algorithms and web-based visualization platforms have provided powerful resources for structural biologists, chemists, and crystallographers, facilitating efficient analysis and reproducibility of experimental outcomes. This review offers a comprehensive overview of contemporary molecular visualization tools, emphasizing their practical applications. Particular attention is given to PyMOL and NGL Viewer, with detailed guidance for their implementation in visualizing proteins, DNA, protein-ligand complexes, protein-protein interactions, protein-DNA assemblies, and small molecule ligands. Challenges frequently encountered in structural biology and cheminformatics, such as the identification of lead compounds for therapeutic development, are also addressed. Molecular dynamics simulations, including binding free energy calculations, are discussed as cost- and time-effective strategies to enhance drug discovery pipelines. In response to the increasing complexity of data-driven research, this review aims to serve as a valuable resource for professionals seeking efficient, reliable visualization tools to support structure-based research and drug design.

METHODS

This review article provides a comprehensive comparative analysis of biomolecular visualization features integrated into standalone and web-based molecular visualization tools. PyMOL (standalone) and NGL (web-based) were systematically employed to visualize proteins, ligands, protein-ligand complexes, protein-protein complexes, and protein-DNA complexes. The methodological framework outlined in this study establishes standardized guidelines for the effective utilization of molecular visualization tools, offering valuable insights for structural biologists and researchers engaged in molecular modeling and structural analysis.

Insight into the binding mechanism between rice characteristic odor compounds and glutelin using multi-spectral and molecular dynamics simulation: Comparison of different functional groups

Aroma quality is an important parameter for evaluating the eating quality of rice. However, the interactions between rice proteins and odor compounds are not well understood. In this study, the combination of computer simulation and instrumental analysis was used to investigate the binding mechanism of odor compounds with rice glutenin for the first time. Hexanal, 1-octen-3-ol, methyl heptenone, and 2-pentylfuran, which have different functional groups and aroma characteristics, were selected as target odor compounds. The results showed that glutelin had the strongest binding affinity for 2-pentylfuran, whereas it was less likely to adsorb 1-octen-3-ol. Static quenching was the main interaction between glutelin and odor compounds. After the interaction, the ordered structure of glutelin changed to a random coil structure. Hydrogen bonds and hydrophobic interactions were the main driving forces for the binding of hexanal, 1-octen-3-ol, and methyl heptenone to glutelin, whereas hydrophobic interactions were the main driving forces for 2-pentylfuran binding. The amino acids TRY, LYS, GLN, and ARG played key roles in the binding. An improved understanding of the binding effect of glutelin on the characteristic odor compounds of rice could help flavor scientists or product designers to optimize the rice flavor.

The physics-AI dialogue in drug design

A long path has led from the determination of the first protein structure in 1960 to the recent breakthroughs in protein science. Protein structure prediction and design methodologies based on machine learning (ML) have been recognized with the 2024 Nobel prize in Chemistry, but they would not have been possible without previous work and the input of many domain scientists. Challenges remain in the application of ML tools for the prediction of structural ensembles and their usage within the software pipelines for structure determination by crystallography or cryogenic electron microscopy. In the drug discovery workflow, ML techniques are being used in diverse areas such as scoring of docked poses, or the generation of molecular descriptors. As the ML techniques become more widespread, novel applications emerge which can profit from the large amounts of data available. Nevertheless, it is essential to balance the potential advantages against the environmental costs of ML deployment to decide if and when it is best to apply it. For hit to lead optimization ML tools can efficiently interpolate between compounds in large chemical series but free energy calculations by molecular dynamics simulations seem to be superior for designing novel derivatives. Importantly, the potential complementarity and/or synergism of physics-based methods (e.g., force field-based simulation models) and data-hungry ML techniques is growing strongly. Current ML methods have evolved from decades of research. It is now necessary for biologists, physicists, and computer scientists to fully understand advantages and limitations of ML techniques to ensure that the complementarity of physics-based methods and ML tools can be fully exploited for drug design.

Genome-wide identification and functional analysis of the longan CONSTANS (CO) family

Longans are among the most economically important subtropical fruits. Its flowering is sensitive to the photoperiod, and flowering time has a significant influence on yield and quality. CONSTANS-like (COL) gene plays a key role in regulating induced flowering in longans. However, the specific role of the COL gene family in the regulation of flowering remains unknown. In this study, 10 DlCOL genes were identified in longans using comprehensive bioinformatics analysis and named based on their physical chromosomal locations. Phylogenetic tree analysis showed that DlCOL genes were divided into three subfamilies, each with a conserved domain. When combined with collinearity analysis, we found DlCOL genes were more closely related to COL genes of dicotyledons. DlCOL family genes are differentially expressed in various longan organs, with DlCOL1, DlCOL3, and DlCOL9 expressed in all organs, with the highest expression levels in floral buds. In the differential expression at different flowering induction stages of 'Sijimi' ('SJ') or 'Shixia' longan ('SX'), DlCOL4 expression was upregulated by 3-fold at the "T1-T2" flowering induction stage in 'SJ', but there was no expression during the three flowering induction stages in 'SX'. Subcellular localization analysis indicated that DlCOL4 is localized in the nucleus. Heterologous transformation of Arabidopsis indicated that DlCOL4 can negatively regulate flowering in transgenic plants. The qRT-PCR (Quantitative real-time PCR) results related to flowering genes indicated that DICOL4 may inhibit flowering by interacting with AtTFL and AtCOL. This study demonstrates the potential functional role of the DlCOL gene and the key role of DlCOL4 in regulating longan flowering.

In-silico evaluation of potential plant-based tyrosinase inhibitors for cosmetic and pharmaceutical applications

Tyrosinase is involved in a critical step of melanin synthesis; therefore, tyrosinase inhibitors are gaining more importance in the medicinal and cosmetic industry for the treatment of different pigmentary disorders. In the last decades, mushroom tyrosinase was used as a standard enzyme for the identification and advancement of most tyrosinase inhibitors. Due to differences in structure and substrate specificity between mushroom and human tyrosinase, there is a need for a more specific study with human tyrosinase. Additionally, the tyrosinase inhibitors which are currently in use have various side effects, therefore, safer inhibitors from natural sources are required. Different tyrosinase inhibitors from natural sources (aloesin, norartocarpetin, hesperetin, morin and taxifolin) were evaluated for an effective eco-friendly whitening agent using different bioinformatics tools. To check the efficacy and safety of the selected compounds ADME analysis was performed which showed that all the selected compounds fulfilled most of the parameters of general drug discovery. Docking of selected ligands was performed against the predicted structure of human tyrosinase; and the binding affinity (in kcal/mol) of kojic acid, aloesin, norartocarpetin, hesperetin, morin and taxifolin were obtained to be - 5.6, - 7.2, - 7.6, - 7.5, - 7.3 and - 7.2 respectively. Among all the selected ligands, norartocarpetin had the lowest binding affinity, i.e., - 7.6 kcal/mol, which showed that norartocarpetin could be used as a potent tyrosinase inhibitor. This bioactive compound is widely distributed in Moraceae plants and therefore, poses as a natural solution to various melanin-based dermatological issues and it can have a potential application in pharmaceuticals and cosmetic industries for the treatment of pigmentary disorders.

Construction of robust protein nanocage by designed disulfide bonds for active cargo molecules protection in the gastric environment

Notwithstanding the progress made, cargo molecules encapsulated within ferritin via oral administration in the gastric environment remains a persistent challenge. This study focuses on the strategic enhancement of ferritin stability in harsh gastric environment. By taking advantagie of computational-assisted design, we strategically introduced up to 96 disulfide bonds along three key inter-subunit interfaces to one single ferritin molecule with human H-chain ferritin and shrimp (Marsupenaeus japonicus) ferritin as starting materials, producing two kinds of robust ferritin nanocages with markedly enhanced acid and protease (pepsin and rennin) resistance. The crystal structure of ferritin nanocage confirmed our design at an atomic level. Encapsulation experiments demonstrated successful loading of bioactive cargo molecules (e.g., doxorubicin) into the engineered ferritin nanocages, with pronouncedly improved protection against leakage under acidic condition and the presence of pepsin and rennin as compared to their native counterparts. This study presents a potential approach for the design and engineering of protein nanocages for oral administration.

AlphaFold2 and ESMFold: A large-scale pairwise model comparison of human enzymes upon Pfam functional annotation

AlphaFold2 predicts protein structures from structural and functional knowledge. Alternatively, ESMFold does the same adopting protein language models. Here, we map available Pfam domains on pairs of models of the human reference proteome computed with both procedures and we compare the mapped regions relevant for functional annotation. We find that, rather irrespectively of the global superimposition of the pairwise models, Pfam-containing regions overlap with a TM-score above 0.8 and a predicted local distance difference test (pLDDT) which is higher than the rest of the modeled sequence. This indicates that both methods are similarly performing in modeled regions that overlap Pfam domains, carrying structural and functional information, with pLDDT values slightly higher for AlphaFold2. The mapping of 9834 Pfam domains also allows the location of 2578 active sites in 3382 enzymes of the human proteome, including 807 proteins for which the active site is not reported in UniProt.

From Gene to Structure: Unraveling Genomic Dark Matter in Ca. Accumulibacter

"Candidatus Accumulibacter" is a unique and pivotal genus of polyphosphate-accumulating organisms prevalent in wastewater treatment plants and plays mainstay roles in the global phosphorus cycle. However, the efforts to fully understand their genetic and metabolic characteristics are largely hindered by major limitations in existing sequence-based annotation methods. Here, we reported an integrated approach combining pangenome analysis, protein structure prediction and clustering, and meta-omic characterization, to uncover genetic and metabolic traits previously unexplored for Ca. Accumulibacter. The identification of a previously overlooked pyrophosphate-fructose 6-phosphate 1-phosphotransferase gene (pfp) suggested that all Ca. Accumulibacter encoded a complete Embden-Meyerhof-Parnas pathway. A homologue of the phosphate-specific transport system accessory protein (PhoU) was suggested to be an inorganic phosphate transport (Pit) accessory protein (Pap) conferring effective and efficient phosphate transport. Additional lineage members were found to encode complete denitrification pathways. A pipeline was built, generating a pan-Ca. Accumulibacter annotation reference database, covering >200,000 proteins and their encoding genes. Benchmarking on 27 Ca. Accumulibacter genomes showed major improvement in the average annotation coverage from 51% to 82%. This pipeline is readily applicable to diverse cultured and uncultured bacteria to establish high-coverage annotation reference databases, facilitating the exploration of genomic dark matter in the bacterial domain.

Reviewability and supportability: New complementary principles to empower research software practices

In today's scientific landscape, research software has evolved from being a supportive tool to becoming a fundamental driver of discovery, particularly in life sciences. Beyond its roots in software engineering, research software now plays a crucial role in facilitating efficient data analysis and enabling the exploration of complex natural phenomena. The advancements in simulations and modeling through research software have significantly accelerated the pace of scientific research while reducing associated costs. This growing reliance underscores the importance of software in ensuring reproducibility - a cornerstone of scientific rigor and trustworthiness. Although verifying reproducibility presents challenges, well-developed and openly accessible research software enhances transparency and aids in the early detection of errors. Although verifying reproducibility can be challenging, well-developed and accessible research software improves transparency and facilitates error detection. This mini-review examines the characteristics of research software and summarizes the key events that have shaped its development, alongside changes in requirements and guidelines. Moreover, we propose two additional principles - reviewability and supportability - complementing the widely accepted FAIR principles (Findability, Accessibility, Interoperability, and Reusability). These new principles aim to improve the efficiency and effectiveness of software evaluation during the peer review process. Through this review, we aim to assist scientists, especially those without extensive software development expertise, in understanding best practices for developing research software and the underlying motivations driving these practices.

In Vitro and Computational Response of Differential Catalysis by Phlebia brevispora BAFC 633 Laccase in Interaction with 2,4-D and Chlorpyrifos

Enzymes secreted by white rot fungi (WRF), such as laccase, offer a promising approach for the treatment of hazardous xenobiotic compounds. This study conducted a comprehensive analysis of the impact of the pesticides 2,4-dichlorophenoxyacetic acid (2,4-D) and chlorpyrifos on the laccase of Phlebia brevispora BAFC 633 through in vitro and bioinformatics analyses. The fungal strain was shown to be tolerant to both pesticides, with notable morphological and ultrastructural alterations in the mycelium. Laccase activity and two isoenzymes (53 and 70 kDa) were detected in all initial treatments. The laccase was concentrated for subsequent catalytic evaluation in the presence of both pesticides, showing high stability at a pH of 3.6 and a temperature range of 50-60 °C. The lacI gene, corresponding to this laccase, was modeled, and its structure revealed a defined catalytic pocket validated with a drug score of 0.61. Molecular docking estimated affinity energies of -5.06 and -9.41 Kcal mol-1 for 2,4-D and chlorpyrifos, respectively. Molecular Mechanics Poisson-Boltzmann Surface Area (MM/PBSA) analysis through 250 ns of molecular dynamics revealed stronger hydrophobic interactions of laccase with chlorpyrifos and highlighted the importance of residue His460 in stabilizing both complexes. Understanding the impact of these agrochemicals on the catalytic function of laccase is crucial for developing future biotechnological strategies involving this enzyme.

Biochemical analysis of packing and assembling heptad repeat motifs in the coronavirus spike protein trimer

During a coronavirus infection, the spike protein undergoes sequential structural transitions triggered by its receptor and the host protease at the interface between the virus and cell membranes, thereby mediating membrane fusion. After receptor binding, the heptad repeat motif (HR1/HR2) within the viral spike protein bridges the viral and cellular membranes; however, the intermediate conformation adopted by the spike protein when drawing the viral and cellular membranes into close proximity remains unclear due to its transient and unstable nature. Here, we experimentally induced conformational changes in the spike protein of a murine coronavirus by incubating the virus with its receptor, followed by exposure to trypsin. We then treated the virus/receptor complex with proteinase K to probe the tightly packed core structure of the spike protein. The conformations of the spike protein were predicted from the sizes of the protease digestion products detected by western blot analysis. Upon receptor binding, two bands (each showing different reactivity with a fusion-inhibiting HR2-peptide) were detected; we propose that these bands correspond to the packed and unpacked HR1/HR2 motifs. After trypsin-mediated triggering, measurement of temperature and time dependency revealed that packing of the remaining unpacked HR1/HR2 motifs and assembly of three HR1 motifs in a trimer occur almost simultaneously. Thus, the trimeric spike protein adopts an asymmetric-unassembled conformation after receptor binding, followed by direct assembly into the post-fusion form triggered by the host protease. This biochemical study provides mechanistic insight into the previously unknown intermediate structure of the viral fusion protein.IMPORTANCEDuring infection by an enveloped virus, receptor binding triggers fusion between the cellular membrane and the virus envelope, enabling delivery of the viral genome to the cytoplasm. The viral spike protein mediates membrane fusion; however the molecular mechanism underlying this process is unclear. This is because using structural biology methods to track the transient conformational changes induced in the unstable spike trimer is challenging. Here, we harnessed the ability of protease enzymes to recognize subtle differences on protein surfaces, allowing us to detect structural differences in the spike protein before and after conformational changes. Differences in the size of the degradation products were analyzed by western blot analysis. The proposed model explaining the conformational changes presented herein is a plausible candidate that provides valuable insight into unanswered questions in the field of virology.

Catalytic function of the laccase enzyme in response to chlorpyrifos and 2,4-dichlorophenoxyacetic acid: behavior in controlled and simulated environments

Enzymes secreted by white-rot fungi, such as laccase, offer a promising solution for treating xenobiotic compounds dangerous to the environment and human health. This study aimed to perform a comprehensive analysis of the tolerance of Pleurotus pulmonarius LBM 105 and its laccase activity toward the pesticides 2,4-D and chlorpyrifos both in vitro and in silico. The fungal strain was able to grow in different concentrations of the pesticides, showing evident morphological alterations. Laccase activity and a 53 kDa electromorph were present in all treatments, showing significant stability with peak activity achieved at a pH of 5.6 and within a temperature range of 50-60 °C. Three laccase genes were mapped, annotated, and characterized from the genome. PplacI obtained better structural validation and affinity energy of - 5.05 and - 7.65 kcal mol-1 with 2,4-D and chlorpyrifos, respectively. The Molecular Mechanics/Poisson-Boltzmann Surface Area analysis at 250 ns confirmed the docking results, revealing the existence of stronger hydrophobic interactions between laccase and chlorpyrifos and highlighting the importance of the Phe341 residue in stabilizing both complexes. Understanding the impact of pesticides on laccase's catalytic function is key to formulating and applying future biotechnological strategies with this enzyme.

qProtein: Exploring Physical Features of Protein Thermostability Based on Structural Proteomics

Thermostability, which is essential for the functional performance of enzymes, is largely determined by intramolecular physical interactions. Although many tools have been developed, existing computational methods have struggled to find the universal principles of protein thermostability. Recent advancements in structural proteomics have been driven by the introduction of deep neural networks such as AlphaFold2 and ESMFold. These innovations have enabled the characterization of protein structures with unprecedented speed and accuracy. Here, we introduce qProtein, a Python-implemented workflow designed for the quantitative analysis of physical interactions on the scale of structural proteomics. This platform accepts protein sequences as input and produces four structural features, including hydrophobic clusters, hydrogen bonds, electrostatic interactions, and disulfide bonds. To demonstrate the use of qProtein, we investigate the structural features related to protein thermostability in six glycoside hydrolase (GH) families, comprising a total of 3,811 protein structures. Our results indicate that in five enzyme families (GH11, GH12, GH5_2, GH10, and GH48), the thermophilic enzymes have a larger average area of hydrophobic clusters compared to the nonthermophilic enzymes within each family. Furthermore, our analysis of the local-structure regions reveals that the hydrophobic clusters are predominantly distributed in the distal regions of the GH11 enzymes. In addition, the average hydrophobic cluster area of the thermophilic enzymes is significantly higher than that of the nonthermophilic enzymes in the distal regions of the GH11 enzymes. Therefore, qProtein is a well-suited platform for analyzing the structural features of thermal stability at the level of structural proteomics. We provide the source code for qProtein at https://github.com/bj600800/qProtein, and the web server is available at http://qProtein.sdu.edu.cn:8888.

Improving viral annotation with artificial intelligence

Viruses of bacteria, "phages," are fundamental, poorly understood components of microbial community structure and function. Additionally, their dependence on hosts for replication positions phages as unique sensors of ecosystem features and environmental pressures. High-throughput sequencing approaches have begun to give us access to the diversity and range of phage populations in complex microbial community samples, and metagenomics is currently the primary tool with which we study phage populations. The study of phages by metagenomic sequencing, however, is fundamentally limited by viral diversity, which results in the vast majority of viral genomes and metagenome-annotated genomes lacking annotation. To harness bacteriophages for applications in human and environmental health and disease, we need new methods to organize and annotate viral sequence diversity. We recently demonstrated that methods that leverage self-supervised representation learning can supplement statistical sequence representations for remote viral protein homology detection in the ocean virome and propose that consideration of the functional content of viral sequences allows for the identification of similarity in otherwise sequence-diverse viruses and viral-like elements for biological discovery. In this review, we describe the potential and pitfalls of large language models for viral annotation. We describe the need for new approaches to annotate viral sequences in metagenomes, the fundamentals of what protein language models are and how one can use them for sequence annotation, the strengths and weaknesses of these models, and future directions toward developing better models for viral annotation more broadly.

Special Issue: "Molecular Dynamics Simulations and Structural Analysis of Protein Domains"

The 3D protein structure is the basis for all their biological functions [...].

Collagen-binding bone morphogenetic protein-2 designed for use in bone tissue engineering

Bone tissue engineering using biodegradable porous scaffolds is a promising approach for restoring oral and maxillofacial bone defects. Recently, attempts have been made to incorporate proteins such as growth factors to create bioactive scaffolds that can engage cells to promote tissue formation. Collagen-based scaffolds containing bone morphogenetic protein-2 (BMP2) have been studied for bone formation. However, controlling the initial burst of BMP2 remains difficult. Here we designed a functional chimeric protein composed of BMP2 and a collagen-binding domain (CBD), specifically the A3 domain of von Willebrand factor, to sustain BMP2 release from collagen-based scaffolds. Based on the results of computer-based structural prediction, we prepared a chimeric protein consisting of CBD and BMP2 in this order with a peptide tag for affinity purification. The chimeric protein had a collagen-binding capacity and enhanced osteogenic differentiation of human mesenchymal stem cells. These results are consistent with insights from in silico structural prediction.

Artificial Intelligence Learns Protein Prediction

From AlphaGO over StableDiffusion to ChatGPT, the recent decade of exponential advances in artificial intelligence (AI) has been altering life. In parallel, advances in computational biology are beginning to decode the language of life: AlphaFold2 leaped forward in protein structure prediction, and protein language models (pLMs) replaced expertise and evolutionary information from multiple sequence alignments with information learned from reoccurring patterns in databases of billions of proteins without experimental annotations other than the amino acid sequences. None of those tools could have been developed 10 years ago; all will increase the wealth of experimental data and speed up the cycle from idea to proof. AI is affecting molecular and medical biology at giant steps, and the most important might be the leap toward more powerful protein design.

Comparative structural studies on Bovine papillomavirus E6 oncoproteins: Novel insights into viral infection and cell transformation from homology modeling and molecular dynamics simulations

Bovine papillomavirus (BPV) infects cattle cells worldwide, leading to hyperproliferative lesions and the potential development of cancer, driven by E5, E6, and E7 oncoproteins along with other cofactors. E6 oncoprotein binds experimentally to various proteins, primarily paxillin and MAML1, as well as hMCM7 and CBP/p300. However, the molecular and structural mechanisms underlying BPV-induced malignant transformation remain unclear. Therefore, we have modeled the E6 oncoprotein structure from non-oncogenic BPV-5 and compared them with oncogenic BPV-1 to assess the relationship between structural features and oncogenic potential. Our analysis elucidated crucial structural aspects of E6, highlighting both conserved elements across genotypes and genotype-specific variations potentially implicated in the oncogenic process, particularly concerning primary target interactions. Additionally, we predicted the location of the hMCM7 binding site on the N-terminal of BPV-5 E6. This study enhances our understanding of the structural characteristics of BPV E6 oncoproteins and their interactions with host proteins, clarifying structural differences and similarities between high and low-risk BPVs. This is important to understand better the mechanisms involved in cell transformation in BPV infection, which could be used as a possible target for therapy.

Fast, sensitive detection of protein homologs using deep dense retrieval

The identification of protein homologs in large databases using conventional methods, such as protein sequence comparison, often misses remote homologs. Here, we offer an ultrafast, highly sensitive method, dense homolog retriever (DHR), for detecting homologs on the basis of a protein language model and dense retrieval techniques. Its dual-encoder architecture generates different embeddings for the same protein sequence and easily locates homologs by comparing these representations. Its alignment-free nature improves speed and the protein language model incorporates rich evolutionary and structural information within DHR embeddings. DHR achieves a >10% increase in sensitivity compared to previous methods and a >56% increase in sensitivity at the superfamily level for samples that are challenging to identify using alignment-based approaches. It is up to 22 times faster than traditional methods such as PSI-BLAST and DIAMOND and up to 28,700 times faster than HMMER. The new remote homologs exclusively found by DHR are useful for revealing connections between well-characterized proteins and improving our knowledge of protein evolution, structure and function.

AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors

The rational design, activity prediction, and adaptive application of biological elements (bio-elements) are crucial research fields in synthetic biology. Currently, a major challenge in the field is efficiently designing desired bio-elements and accurately predicting their activity using vast datasets. The advancement of artificial intelligence (AI) technology has enabled machine learning and deep learning algorithms to excel in uncovering patterns in bio-element data and predicting their performance. This review explores the application of AI algorithms in the rational design of bio-elements, activity prediction, and the regulation of transcription-factor-based biosensor response performance using AI-designed elements. We discuss the advantages, adaptability, and biological challenges addressed by the AI algorithms in various applications, highlighting their powerful potential in analyzing biological data. Furthermore, we propose innovative solutions to the challenges faced by AI algorithms in the field and suggest future research directions. By consolidating current research and demonstrating the practical applications and future potential of AI in synthetic biology, this review provides valuable insights for advancing both academic research and practical applications in biotechnology.

Structure based functional identification of an uncharacterized protein from Coxiella burnetii involved in adipogenesis

Coxiella burnetii, the causative agent of Q fever, is an intracellular pathogen posing a significant global public health threat. There is a pressing need for dependable and effective treatments, alongside an urgency for further research into the molecular characterization of its genome. Within the genomic landscape of Coxiella burnetii, numerous hypothetical proteins remain unidentified, underscoring the necessity for in-depth study. In this study, we conducted comprehensive in silico analyses to identify and prioritize potential hypothetical protein of Coxiella burnetii, aiming to elucidate the structure and function of uncharacterized protein. Furthermore, we delved into the physicochemical properties, localization, and molecular dynamics and simulations, and assessed the primary, secondary, and tertiary structures employing a variety of bioinformatics tools. The in-silico analysis revealed that the uncharacterized protein contains a conserved Mth938-like domain, suggesting a role in preadipocyte differentiation and adipogenesis. Subcellular localization predictions indicated its presence in the cytoplasm, implicating a significant role in cellular processes. Virtual screening identified ligands with high binding affinities, suggesting the protein's potential as a drug target against Q fever. Molecular dynamics simulations confirmed the stability of these complexes, indicating their therapeutic relevance. The findings provide a structural and functional overview of an uncharacterized protein from C. burnetii, implicating it in adipogenesis. This study underscores the power of in-silico approaches in uncovering the biological roles of uncharacterized proteins and facilitating the discovery of new therapeutic strategies. The findings provide valuable preliminary data for further investigation into the protein's role in adipogenesis.

Based on hydrogen and disulfide-mediated bonds, l-lysine and l-arginine enhanced the gel properties of low-salt mixed shrimp surimi (Antarctic krill and Pacific white shrimp)

This study delved into the effects of l-lysine (Lys) and l-arginine (Arg) on the gel properties and intermolecular interactions of low-salt (NaCl, 1 g/100 g) mixed shrimp surimi (Antarctic krill and Pacific white shrimp). The addition of Lys and Arg improved the gel strength and water holding capacity of low-salt gels, which were superior to the properties of STPP and high-salt (NaCl, 2.25 g/100 g) gels. These results can be attributed to the role of Lys and Arg in enhancing hydrogen and disulfide bonds within the low-salt gel system, promoting the solubilization of myofibrillar proteins (MP) and consequently increasing the number of MP molecules participating in gel formation. Antarctic krill MP did not show gel-forming ability and exerted a diluting effect on low-salt mixed shrimp surimi gels. Molecular docking analysis indicated the stable binding of Lys and Arg to myosin.

Site-specific mutagenesis screening in KRASG12D mutant library to uncover resistance mechanisms to KRASG12D inhibitors

KRAS plays a crucial role in regulating cell survival and proliferation and is one of the most commonly mutated oncogenes in human cancers. The novel KRASG12D inhibitor, MRTX1133, demonstrates promising antitumor efficacy in vitro and in vivo. However, the development of acquired resistance in treated patients presents a considerable challenge to sustained therapeutic effectiveness. In response to this challenge, we conducted site-specific mutagenesis screening to identify potential secondary mutations that could induce resistance to MRTX1133. We screened a range of KRASG12D variants harboring potential secondary mutations, and 44 representative variants were selected for in-depth validation of the pooled screening outcomes. We identified eight variants (G12D with V9E, V9W, V9Q, G13P, T58Y, R68G, Y96W, and Q99L) that exhibited substantial resistance, with V9W showing notable resistance, and downstream signaling analyses and structural modeling were conducted. We observed that secondary mutations in KRASG12D can lead to acquired resistance to MRTX1133 and BI-2865, a novel pan-KRAS inhibitor, in human cancer cell lines. This evidence is critical for devising new strategies to counteract resistance mechanisms and, ultimately, enhance treatment outcomes in patients with KRASG12D-mutant cancers.

Effect of chlorpyrifos-exposure on the expression levels of CYP genes in Daphnia magna and examination of a possibility that an up-regulated clan 3 CYP, CYP360A8, reacts with pesticides

Daphnia magna is a test organism used for ecological risk assessments of pesticides, but little is known about the expression levels of cytochrome P450s (CYP)s and their changes after pesticide exposure in the less than 24-h-olds used for ecotoxicity tests. In this study, D. magna juveniles were exposed to 0.2 μg/L of chlorpyrifos under the conditions for acute immobilization test as specified by the OECD test guideline for 24 h, and then the gene expression was compared between the control and chlorpyrifos-exposure groups by RNA-sequencing analysis, with a focus on CYP genes. Among 38 CYP genes expressed in the control group, seven were significantly up-regulated while two were significantly down-regulated in the chlorpyrifos-exposure group. Although the sublethal concentration of chlorpyrifos did not change their expression levels so drastically (0.8 < fold change < 2.6), CY360A8 of D. magna (DmCYP360A8), which had been proposed to be responsible for metabolism of xenobiotics, was abundantly expressed in controls yet up-regulated by chlorpyrifos. Therefore, homology modeling of DmCYP360A8 was performed based on the amino acid sequence, and then molecular docking simulations with the insecticides that were indicated to be metabolized by CYPs in D. magna were conducted. The results indicated that DmCYP360A8 could contribute to the metabolism of diazinon and chlorfenapyr but not chlorpyrifos. These findings suggest that chlorpyrifos is probably detoxified by other CYP(s) including up-regulated and/or constitutively expressed one(s).

Structure and function of engineered stromal cell-derived factor-1α

To design biologically active, collagen-based scaffolds for bone tissue engineering, we have synthesized chimeric proteins consisting of stromal cell-derived factor-1α (SDF) and the von Willebrand factor A3 collagen-binding domain (CBD). The chimeric proteins were used to evaluate the effect of domain linkage and its order on the structure and function of the SDF and CBD. The structure of the chimeric proteins was analyzed by circular dichroism spectroscopy, while functional analysis was performed by a cell migration assay for the SDF domain and a collagen-binding assay for the CBD domain. Furthermore, computational structural prediction was conducted for the chimeric proteins to examine the consistency with the results of structural and functional analyses. Our structural and functional analyses as well as structural prediction revealed that linking two domains can affect their functions. However, their order had minor effects on the three-dimensional structure of CBD and SDF in the chimeric proteins.

Computational Workflow for Refining AlphaFold Models in Drug Design Using Kinetic and Thermodynamic Binding Calculations: A Case Study for the Unresolved Inactive Human Adenosine A3 Receptor

A structure-based drug design pipeline that considers both thermodynamic and kinetic binding data of ligands against a receptor will enable the computational design of improved drug molecules. For unresolved GPCR-ligand complexes, a workflow that can apply both thermodynamic and kinetic binding data in combination with alpha-fold (AF)-derived or other homology models and experimentally resolved binding modes of relevant ligands in GPCR-homologs needs to be tested. Here, as test case, we studied a congeneric set of ligands that bind to a structurally unresolved G protein-coupled receptor (GPCR), the inactive human adenosine A3 receptor (hA3R). We tested three available homology models from which two have been generated from experimental structures of hA1R or hA2AR and one model was a multistate alphafold 2 (AF2)-derived model. We applied alchemical calculations with thermodynamic integration coupled with molecular dynamics (TI/MD) simulations to calculate the experimental relative binding free energies and residence time (τ)-random accelerated MD (τ-RAMD) simulations to calculate the relative residence times (RTs) for antagonists. While the TI/MD calculations produced, for the three homology models, good Pearson correlation coefficients, correspondingly, r = 0.74, 0.62, and 0.67 and mean unsigned error (mue) values of 0.94, 1.31, and 0.81 kcal mol-1, the τ-RAMD method showed r = 0.92 and 0.52 for the first two models but failed to produce accurate results for the multistate AF2-derived model. With subsequent optimization of the AF2-derived model by reorientation of the side chain of R1735.34 located in the extracellular loop 2 (EL2) that blocked ligand's unbinding, the computational model showed r = 0.84 for kinetic data and improved performance for thermodynamic data (r = 0.81, mue = 0.56 kcal mol-1). Overall, after refining the multistate AF2 model with physics-based tools, we were able to show a strong correlation between predicted and experimental ligand relative residence times and affinities, achieving a level of accuracy comparable to an experimental structure. The computational workflow used can be applied to other receptors, helping to rank candidate drugs in a congeneric series and enabling the prioritization of leads with stronger binding affinities and longer residence times.

Molecular Modeling Techniques and In-Silico Drug Discovery

Molecular modeling is the technique to determine the overall structure of an unknown molecule, be it a small one or a macromolecule. The technique encompasses the method of screening ligand libraries for the development of new candidate drug molecules. All these aspects have become an essential topic of research. This field is truly interdisciplinary and finds its applications in almost all fields of life science research. In this chapter, an overview of the protocol associated with molecular modeling techniques will be discussed.

Characterization of terminal flowering cowpea (Vigna unguiculata (L.) Walp.) mutants obtained by induced mutagenesis digs out the loss-of-function of phosphatidylethanolamine-binding protein

Cowpea (Vigna unguiculata (L.) Walp) is one of the major food legume crops grown extensively in arid and semi-arid regions of the world. The determinate habit of cowpea has many advantages over the indeterminate and is well adapted to modern farming systems. Mutation breeding is an active research area to develop the determinate habit of cowpea. The present study aimed to develop new determinate habit mutants with terminal flowering (TFL) in locally well-adapted genetic backgrounds. Consequently, the seeds of popular cowpea cv P152 were irradiated with doses of gamma rays (200, 250, and, 300 Gy), and the M1 populations were grown. The M2 populations were produced from the M1 progenies and selected determinate mutants (TFLCM-1 and TFLCM-2) from the M2 generation (200 Gy) were forwarded up to the M5 generation to characterize the mutants and simultaneously they were crossed with P152 to develop a MutMap population. In the M5 generation, determinate mutants (80-81 days) were characterized by evaluating the TFL growth habit, longer peduncles (30.75-31.45 cm), erect pods (160°- 200°), number of pods per cluster (4-5 nos.), and early maturity. Further, sequencing analysis of the VuTFL1 gene in the determinate mutants and MutMap population revealed a single nucleotide transversion (A-T at 1196 bp) in the fourth exon and asparagine (N) to tyrosine (Y) amino acid change at the 143rd position of phosphatidylethanolamine-binding protein (PEBP). Notably, the loss of function PEPB with a higher confidence level modification of anti-parallel beta-sheets and destabilization of the protein secondary structure was observed in the mutant lines. Quantitative real-time PCR (qRT-PCR) analysis showed that the VuTFL1 gene was downregulated at the flowering stage in TFL mutants. Collectively, the insights garnered from this study affirm the effectiveness of induced mutation in modifying the plant's ideotype. The TFL mutants developed during this investigation have the potential to serve as a valuable resource for fostering determinate traits in future cowpea breeding programs and pave the way for mechanical harvesting.

Biochemical characterisation of a PL24 ulvan lyase from seaweed-associated Vibrio sp. FNV38

Ulvan is a green macroalgal cell wall polysaccharide that has tremendous potential for valorisation due to its unique composition of sulphated rhamnose, glucuronic acid, iduronic acid and xylose. Several potential applications such as production of biofuels, bioplastics and other value-added products necessitate the breakdown of the polysaccharide to oligomers or monomers. Research on ulvan saccharifying enzymes has been continually increasing over the last decade, with the increasing focus on valorisation of seaweed biomass for a biobased economy. Lyases are the first of several enzymes that are involved in saccharifying the polysaccharide and several ulvan lyases have been structurally and biochemically characterised to enable their effective use in the valorisation processes. This study investigates the whole genome of Vibrio sp. FNV38, an ulvan metabolising organism and biochemical characteristics of a PL24 ulvan lyase that it possesses. The genome of Vibrio sp. FNV38 has a diverse CAZy profile with several genes involved in the metabolism of ulvan, cellulose, agar, and alginate. The enzyme exhibits optimal activity at pH 8.5 in 100 mM Tris-HCl buffer and 30 °C. However, its thermal stability is poor with significant loss of activity after 2 h of incubation at temperatures above 25 °C. Breakdown product analysis reveals that the enzyme depolymerised the polysaccharide predominantly to disaccharides and tetrasaccharides.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10811-023-03136-3.

Computational approaches for modeling and structural design of biological systems: A comprehensive review

The convergence of biology and computational science has ushered in a revolutionary era, revolutionizing our understanding of biological systems and providing novel solutions to global problems. The field of genetic engineering has facilitated the manipulation of genetic codes, thus providing opportunities for the advancement of innovative disease therapies and environmental enhancements. The emergence of bio-molecular simulation represents a significant advancement in this particular field, as it offers the ability to gain microscopic insights into molecular-level biological processes over extended periods. Biomolecular simulation plays a crucial role in advancing our comprehension of organismal mechanisms by establishing connections between molecular structures, interactions, and biological functions. The field of computational biology has demonstrated its significance in deciphering intricate biological enigmas through the utilization of mathematical models and algorithms. The process of decoding the human genome has resulted in the advancement of therapies for a wide range of genetic disorders, while the simulation of biological systems contributes to the identification of novel pharmaceutical compounds. The potential of biomolecular simulation and computational biology is vast and limitless. As the exploration of the underlying principles that govern living organisms progresses, the potential impact of this understanding on cancer treatment, environmental restoration, and other domains is anticipated to be transformative. This review examines the notable advancements achieved in the field of computational biology, emphasizing its potential to revolutionize the comprehension and enhancement of biological systems.

Interaction of narcissoside with α-amylase from Bacillus subtilis and Porcine pancreatic by multi-spectral analysis and molecular dynamics simulation

In this work, interaction mechanism of narcissoside with two α-amylase from Bacillus subtilis (BSA) and Porcine pancreatic (PPA) are comparatively studied by multi-spectral analysis, molecular docking and molecular dynamics simulation. The results prove that narcissoside can statically quench fluorescence of BSA/PPA. Two complexes are mainly formed by hydrogen bond and van der Waals force. With the increase of temperature, the two complexes formed by narcissoside and two enzymes become unstable. At the same experimental temperature, the binding force of narcissoside to PPA is higher than that of BSA. The binding of narcissoside to PPA/BSA increases the hydrophobicity of microenvironment. Moreover, the secondary structure of PPA/BSA is mainly changed by decreasing the α-helix. The optimal binding modes of narcissoside with BSA/PPA are predicted by molecular docking, and the stability of the two complexes is evaluated by molecular dynamics simulations.

In Silico Analysis of the Dextransucrase Obtained From Leuconostoc mesenteroides Strain IBUN 91.2.98

The DSR-IBUN dextransucrase produced by Leuconostoc mesenteroides strain IBUN 91.2.98 has a short production time (4.5 hours), an enzymatic activity of 24.8 U/mL, and a specific activity of purified enzyme 2 times higher (331.6 U/mg) than that reported for similar enzymes. The aim of this study was to generate a structural model that, from an in silico approach, allows a better understanding, from the structural point of view, of the activity obtained by the enzyme of interest, which is key to continue with its study and industry application. For this, we translated the nucleotide sequence of the dsr_IBUN gene. With the primary structure of DSR-IBUN, the in silico prediction of physicochemical parameters, the possible subcellular localization, the presence of signal peptide, and the location of domains and functional and structural motifs of the protein were established. Subsequently, its secondary and tertiary structure were predicted and a homology model of the dextransucrase under study was constructed using Swiss-Model, performing careful template selection. The values obtained for the model, Global Model Quality Estimation (0.63), Quality Mean (-1.49), and root-mean-square deviation (0.09), allow us to affirm that the model for the enzyme dextransucrase DSR-IBUN is of adequate quality and can be used as a source of information for this protein.

Expression, Characterisation, Homology Modelling and Molecular Docking of a Novel M17 Family Leucyl-Aminopeptidase from Bacillus cereus CZ

Leucyl-aminopeptidase (LAP), an important metallopeptidase, hydrolyses amino acid residues from the N-terminus of polypeptides and proteins, acting preferentially on the peptide bond formed by N-terminus leucine. A new leucyl-aminopeptidase was found in Bacillus cereus CZ. Its gene (bclap) contained a 1485 bp ORF encoding 494 amino acids with a molecular weight of 54 kDa. The bcLAP protein was successfully expressed in E. coli BL21(DE3). Optimal activity is obtained at pH 9.0 and 58 °C. The bcLAP displays a moderate thermostability and an alkaline pH adaptation range. Enzymatic activity is dramatically enhanced by Ni2+. EDTA significantly inhibits the enzymatic activity, and bestatin and SDS also show strong inhibition. The three-dimensional model of bcLAP monomer and homohexamer is simulated byPHYRE2 server and SWISS-MODEL server. The docking of bestatin, Leu-Trp, Asp-Trp and Ala-Ala-Gly to bcLAP is performed using AutoDock4.2.5, respectively. Molecular docking results show that the residues Lys260, Asp265, Lys272, Asp283, Asp342, Glu344, Arg346, Gly372 and His437 are involved in the hydrogen bonding with the ligands and zinc ions. There may be two nucleophilic catalytic mechanisms in bcLAP, one involving His 437 or Arg346 and the other involving His437 and Arg346. The bcLAP can hydrolyse the peptide bonds in Leu-Trp, Asp-Trp and Ala-Ala-Gly.

Domain swap facilitates structural transitions of spider silk protein C-terminal domains

Domain swap is a mechanism of protein dimerization where the two interacting domains exchange parts of their structure. Web spiders make use of the process in the connection of C-terminal domains (CTDs) of spidroins, the soluble protein building blocks that form tough silk fibers. Besides providing connectivity and solubility, spidroin CTDs are responsible for inducing structural transitions during passage through an acidified assembly zone within spinning ducts. The underlying molecular mechanisms are elusive. Here, we studied the folding of five homologous spidroin CTDs from different spider species or glands. Four of these are domain-swapped dimers formed by five-helix bundles from spidroins of major and minor ampullate glands. The fifth is a dimer that lacks domain swap, formed by four-helix bundles from a spidroin of a flagelliform gland. Spidroins from this gland do not undergo structural transitions whereas the others do. We found a three-state mechanism of folding and dimerization that was conserved across homologues. In chemical denaturation experiments the native CTD dimer unfolded to a dimeric, partially structured intermediate, followed by full unfolding to denatured monomers. The energetics of the individual folding steps varied between homologues. Contrary to the common belief that domain swap stabilizes protein assemblies, the non-swapped homologue was most stable and folded four orders of magnitude faster than a swapped variant. Domain swap of spidroin CTDs induces an entropic penalty to the folding of peripheral helices, thus unfastening them for acid-induced unfolding within a spinning duct, which primes them for refolding into alternative structures during silk formation.

Systematic identification of conditionally folded intrinsically disordered regions by AlphaFold2

The AlphaFold Protein Structure Database contains predicted structures for millions of proteins. For the majority of human proteins that contain intrinsically disordered regions (IDRs), which do not adopt a stable structure, it is generally assumed that these regions have low AlphaFold2 confidence scores that reflect low-confidence structural predictions. Here, we show that AlphaFold2 assigns confident structures to nearly 15% of human IDRs. By comparison to experimental NMR data for a subset of IDRs that are known to conditionally fold (i.e., upon binding or under other specific conditions), we find that AlphaFold2 often predicts the structure of the conditionally folded state. Based on databases of IDRs that are known to conditionally fold, we estimate that AlphaFold2 can identify conditionally folding IDRs at a precision as high as 88% at a 10% false positive rate, which is remarkable considering that conditionally folded IDR structures were minimally represented in its training data. We find that human disease mutations are nearly fivefold enriched in conditionally folded IDRs over IDRs in general and that up to 80% of IDRs in prokaryotes are predicted to conditionally fold, compared to less than 20% of eukaryotic IDRs. These results indicate that a large majority of IDRs in the proteomes of human and other eukaryotes function in the absence of conditional folding, but the regions that do acquire folds are more sensitive to mutations. We emphasize that the AlphaFold2 predictions do not reveal functionally relevant structural plasticity within IDRs and cannot offer realistic ensemble representations of conditionally folded IDRs.

Structure-Based In Silico Screening of Marine Phlorotannins for Potential Walrus Calicivirus Inhibitor

A new calicivirus isolated from a walrus was reported in 2004. Since unknown marine mammalian zoonotic viruses could pose great risks to human health, this study aimed to develop therapeutic countermeasures to quell any potential outbreak of a pandemic caused by this virus. We first generated a 3D model of the walrus calicivirus capsid protein and identified compounds from marine natural products, especially phlorotannins, as potential walrus calicivirus inhibitors. A 3D model of the target protein was generated using homology modeling based on two publicly available template sequences. The sequence of the capsid protein exhibited 31.3% identity and 42.7% similarity with the reference templates. The accuracy and reliability of the predicted residues were validated via Ramachandran plotting. Molecular docking simulations were performed between the capsid protein 3D model and 17 phlorotannins. Among them, five phlorotannins demonstrated markedly stable docking profiles; in particular, 2,7-phloroglucinol-6,6-bieckol showed favorable structural integrity and stability during molecular dynamics simulations. The results indicate that the phlorotannins are promising walrus calicivirus inhibitors. Overall, the study findings showcase the rapid turnaround of in silico-based drug discovery approaches, providing useful insights for developing potential therapies against novel pathogenic viruses, especially when the 3D structures of the viruses remain experimentally unknown.

E2EDA: Protein Domain Assembly Based on End-to-End Deep Learning

With the development of deep learning, almost all single-domain proteins can be predicted at experimental resolution. However, the structure prediction of multi-domain proteins remains a challenge. Achieving end-to-end protein domain assembly and further improving the accuracy of the full-chain modeling by accurately predicting inter-domain orientation while improving the assembly efficiency will provide significant insights into structure-based drug discovery. In this work, we propose an End-to-End Domain Assembly method based on deep learning, named E2EDA. We first develop RMNet, an EfficientNetV2-based deep learning model that fuses multiple features using an attention mechanism to predict inter-domain rigid motion. Then, the predicted rigid motions are transformed into inter-domain spatial transformations to directly assemble the full-chain model. Finally, the scoring strategy RMscore is designed to select the best model from multiple assembled models. The experimental results show that the average TM-score of the model assembled by E2EDA on the benchmark set (282) is 0.827, which is better than those of other domain assembly methods SADA (0.792) and DEMO (0.730). Meanwhile, on our constructed multi-domain data set from AlphaFold DB, the model reassembled by E2EDA is 7.0% higher in TM-score compared to the full-chain model predicted by AlphaFold2, indicating that E2EDA can capture more accurate inter-domain orientations to improve the quality of the model predicted by AlphaFold2. Furthermore, compared to SADA and AlphaFold2, E2EDA reduced the average runtime on the benchmark by 64.7% and 19.2%, respectively, indicating that E2EDA can significantly improve assembly efficiency through an end-to-end approach. The online server is available at http://zhanglab-bioinf.com/E2EDA.

Functional annotation of haloacid dehalogenase superfamily structural genomics proteins

Haloacid dehalogenases (HAD) are members of a large superfamily that includes many Structural Genomics proteins with poorly characterized functionality. This superfamily consists of multiple types of enzymes that can act as sugar phosphatases, haloacid dehalogenases, phosphonoacetaldehyde hydrolases, ATPases, or phosphate monoesterases. Here, we report on predicted functional annotations and experimental testing by direct biochemical assay for Structural Genomics proteins from the HAD superfamily. To characterize the functions of HAD superfamily members, nine representative HAD proteins and 21 structural genomics proteins are analyzed. Using techniques based on computed chemical and electrostatic properties of individual amino acids, the functions of five structural genomics proteins from the HAD superfamily are predicted and validated by biochemical assays. A dehalogenase-like hydrolase, RSc1362 (Uniprot Q8XZN3, PDB 3UMB) is predicted to be a dehalogenase and dehalogenase activity is confirmed experimentally. Four proteins predicted to be sugar phosphatases are characterized as follows: a sugar phosphatase from Thermophilus volcanium (Uniprot Q978Y6) with trehalose-6-phosphate phosphatase and fructose-6-phosphate phosphatase activity; haloacid dehalogenase-like hydrolase from Bacteroides thetaiotaomicron (Uniprot Q8A2F3; PDB 3NIW) with fructose-6-phosphate phosphatase and sucrose-6-phosphate phosphatase activity; putative phosphatase from Eubacterium rectale (Uniprot D0VWU2; PDB 3DAO) as a sucrose-6-phosphate phosphatase; and hypothetical protein from Geobacillus kaustophilus (Uniprot Q5L139; PDB 2PQ0) as a fructose-6-phosphate phosphatase. Most of these sugar phosphatases showed some substrate promiscuity.

AI/ML combined with next-generation sequencing of VHH immune repertoires enables the rapid identification of de novo humanized and sequence-optimized single domain antibodies: a prospective case study

Introduction: In this study, we demonstrate the feasibility of yeast surface display (YSD) and nextgeneration sequencing (NGS) in combination with artificial intelligence and machine learning methods (AI/ML) for the identification of de novo humanized single domain antibodies (sdAbs) with favorable early developability profiles. Methods: The display library was derived from a novel approach, in which VHH-based CDR3 regions obtained from a llama (Lama glama), immunized against NKp46, were grafted onto a humanized VHH backbone library that was diversified in CDR1 and CDR2. Following NGS analysis of sequence pools from two rounds of fluorescence-activated cell sorting we focused on four sequence clusters based on NGS frequency and enrichment analysis as well as in silico developability assessment. For each cluster, long short-term memory (LSTM) based deep generative models were trained and used for the in silico sampling of new sequences. Sequences were subjected to sequence- and structure-based in silico developability assessment to select a set of less than 10 sequences per cluster for production. Results: As demonstrated by binding kinetics and early developability assessment, this procedure represents a general strategy for the rapid and efficient design of potent and automatically humanized sdAb hits from screening selections with favorable early developability profiles.

Antimicrobial Peptides: The Production of Novel Peptide-Based Therapeutics in Plant Systems

The increased prevalence of antibiotic resistance is alarming and has a significant impact on the economies of emerging and underdeveloped nations. The redundancy of antibiotic discovery platforms (ADPs) and injudicious use of conventional antibiotics has severely impacted millions, across the globe. Potent antimicrobials from biological sources have been extensively explored as a ray of hope to counter the growing menace of antibiotic resistance in the population. Antimicrobial peptides (AMPs) are gaining momentum as powerful antimicrobial therapies to combat drug-resistant bacterial strains. The tremendous therapeutic potential of natural and synthesized AMPs as novel and potent antimicrobials is highlighted by their unique mode of action, as exemplified by multiple research initiatives. Recent advances and developments in antimicrobial discovery and research have increased our understanding of the structure, characteristics, and function of AMPs; nevertheless, knowledge gaps still need to be addressed before these therapeutic options can be fully exploited. This thematic article provides a comprehensive insight into the potential of AMPs as potent arsenals to counter drug-resistant pathogens, a historical overview and recent advances, and their efficient production in plants, defining novel upcoming trends in drug discovery and research. The advances in synthetic biology and plant-based expression systems for AMP production have defined new paradigms in the efficient production of potent antimicrobials in plant systems, a prospective approach to countering drug-resistant pathogens.

RNA contact prediction by data efficient deep learning

On the path to full understanding of the structure-function relationship or even design of RNA, structure prediction would offer an intriguing complement to experimental efforts. Any deep learning on RNA structure, however, is hampered by the sparsity of labeled training data. Utilizing the limited data available, we here focus on predicting spatial adjacencies ("contact maps") as a proxy for 3D structure. Our model, BARNACLE, combines the utilization of unlabeled data through self-supervised pre-training and efficient use of the sparse labeled data through an XGBoost classifier. BARNACLE shows a considerable improvement over both the established classical baseline and a deep neural network. In order to demonstrate that our approach can be applied to tasks with similar data constraints, we show that our findings generalize to the related setting of accessible surface area prediction.

Targeting SLC transporters: small molecules as modulators and therapeutic opportunities

Solute carrier (SLCs) transporters mediate the transport of a broad range of solutes across biological membranes. Dysregulation of SLCs has been associated with various pathologies, including metabolic and neurological disorders, as well as cancer and rare diseases. SLCs are therefore emerging as key targets for therapeutic intervention with several recently approved drugs targeting these proteins. Unlocking this large and complex group of proteins is essential to identifying unknown SLC targets and developing next-generation SLC therapeutics. Recent progress in experimental and computational techniques has significantly advanced SLC research, including drug discovery. Here, we review emerging topics in therapeutic discovery of SLCs, focusing on state-of-the-art approaches in structural, chemical, and computational biology, and discuss current challenges in transporter drug discovery.

Bioinformatic and literature assessment of toxicity and allergenicity of a CRISPR-Cas9 engineered gene drive to control Anopheles gambiae the mosquito vector of human malaria

BACKGROUND

Population suppression gene drive is currently being evaluated, including via environmental risk assessment (ERA), for malaria vector control. One such gene drive involves the dsxFCRISPRh transgene encoding (i) hCas9 endonuclease, (ii) T1 guide RNA (gRNA) targeting the doublesex locus, and (iii) DsRed fluorescent marker protein, in genetically-modified mosquitoes (GMMs). Problem formulation, the first stage of ERA, for environmental releases of dsxFCRISPRh previously identified nine potential harms to the environment or health that could occur, should expressed products of the transgene cause allergenicity or toxicity.

METHODS

Amino acid sequences of hCas9 and DsRed were interrogated against those of toxins or allergens from NCBI, UniProt, COMPARE and AllergenOnline bioinformatic databases and the gRNA was compared with microRNAs from the miRBase database for potential impacts on gene expression associated with toxicity or allergenicity. PubMed was also searched for any evidence of toxicity or allergenicity of Cas9 or DsRed, or of the donor organisms from which these products were originally derived.

RESULTS

While Cas9 nuclease activity can be toxic to some cell types in vitro and hCas9 was found to share homology with the prokaryotic toxin VapC, there was no evidence from previous studies of a risk of toxicity to humans and other animals from hCas9. Although hCas9 did contain an 8-mer epitope found in the latex allergen Hev b 9, the full amino acid sequence of hCas9 was not homologous to any known allergens. Combined with a lack of evidence in the literature of Cas9 allergenicity, this indicated negligible risk to humans of allergenicity from hCas9. No matches were found between the gRNA and microRNAs from either Anopheles or humans. Moreover, potential exposure to dsxFCRISPRh transgenic proteins from environmental releases was assessed as negligible.

CONCLUSIONS

Bioinformatic and literature assessments found no convincing evidence to suggest that transgenic products expressed from dsxFCRISPRh were allergens or toxins, indicating that environmental releases of this population suppression gene drive for malaria vector control should not result in any increased allergenicity or toxicity in humans or animals. These results should also inform evaluations of other GMMs being developed for vector control and in vivo clinical applications of CRISPR-Cas9.

ModelCIF: An Extension of PDBx/mmCIF Data Representation for Computed Structure Models

ModelCIF (github.com/ihmwg/ModelCIF) is a data information framework developed for and by computational structural biologists to enable delivery of Findable, Accessible, Interoperable, and Reusable (FAIR) data to users worldwide. ModelCIF describes the specific set of attributes and metadata associated with macromolecular structures modeled by solely computational methods and provides an extensible data representation for deposition, archiving, and public dissemination of predicted three-dimensional (3D) models of macromolecules. It is an extension of the Protein Data Bank Exchange / macromolecular Crystallographic Information Framework (PDBx/mmCIF), which is the global data standard for representing experimentally-determined 3D structures of macromolecules and associated metadata. The PDBx/mmCIF framework and its extensions (e.g., ModelCIF) are managed by the Worldwide Protein Data Bank partnership (wwPDB, wwpdb.org) in collaboration with relevant community stakeholders such as the wwPDB ModelCIF Working Group (wwpdb.org/task/modelcif). This semantically rich and extensible data framework for representing computed structure models (CSMs) accelerates the pace of scientific discovery. Herein, we describe the architecture, contents, and governance of ModelCIF, and tools and processes for maintaining and extending the data standard. Community tools and software libraries that support ModelCIF are also described.

High-throughput screening and investigation of the inhibitory mechanism of α-glucosidase inhibitors in teas using an affinity selection-mass spectrometry method

An affinity selection-mass spectrometry method was applied for high-throughput screening of α-glucosidase (AGH) inhibitors from teas. Fourteen out of nineteen screened AGH inhibitor candidates were clustered as galloylated polyphenols (GPs). "AGH-GPs" interaction studies, including enzyme kinetics, fluorescence spectroscopy, circular dichroism, and molecular docking, jointly suggested that GPs noncompetitively inhibit AGH activity by interacting with amino acid residues near the active site of AGH and inducing changes in AGH secondary structure. Representative GPs and white tea extract (WTE) showed comparable AGH inhibition effects in Caco2 cells and postprandial hypoglycemic efficacy in diabetic mice as acarbose. The area under the curve of oral sucrose tolerance test was lower by 8.16%, 6.17%, and 7.37% than control group in 15 mg/kg EGCG, 15 mg/kg strictinin, and 150 mg/kg WTE group, respectively. Our study presents a high-efficiency approach to discover novel AGH inhibitors and elucidates a potential mechanism by which tea decreases diabetes risks.

An agnostic analysis of the human AlphaFold2 proteome using local protein conformations

Knowledge of the 3D structure of proteins is a valuable asset for understanding their precise biological mechanisms. However, the cost of production of 3D structures and experimental difficulties limit their obtaining. The proposal of 3D structural models is consequently an appealing alternative. The release of the AlphaFold Deep Learning approach has revolutionized the field. The recent near-complete human proteome proposal makes it possible to analyse large amounts of data and evaluate the results of the approach in greater depth. The 3D human proteome was thus analysed in light of the classic secondary structures, and many less-used protein local conformations (PolyProline II helices, type of γ-turns, of β-turns and of β-bulges, curvature of the helices, and a structural alphabet). Without questioning the global quality of the approach, this analysis highlights certain local conformations, which maybe poorly predicted and they could therefore be better addressed.

Exploring microbial functional biodiversity at the protein family level-From metagenomic sequence reads to annotated protein clusters

Metagenomics has enabled accessing the genetic repertoire of natural microbial communities. Metagenome shotgun sequencing has become the method of choice for studying and classifying microorganisms from various environments. To this end, several methods have been developed to process and analyze the sequence data from raw reads to end-products such as predicted protein sequences or families. In this article, we provide a thorough review to simplify such processes and discuss the alternative methodologies that can be followed in order to explore biodiversity at the protein family level. We provide details for analysis tools and we comment on their scalability as well as their advantages and disadvantages. Finally, we report the available data repositories and recommend various approaches for protein family annotation related to phylogenetic distribution, structure prediction and metadata enrichment.

Protein Design Using Physics Informed Neural Networks

The inverse protein folding problem, also known as protein sequence design, seeks to predict an amino acid sequence that folds into a specific structure and performs a specific function. Recent advancements in machine learning techniques have been successful in generating functional sequences, outperforming previous energy function-based methods. However, these machine learning methods are limited in their interoperability and robustness, especially when designing proteins that must function under non-ambient conditions, such as high temperature, extreme pH, or in various ionic solvents. To address this issue, we propose a new Physics-Informed Neural Networks (PINNs)-based protein sequence design approach. Our approach combines all-atom molecular dynamics simulations, a PINNs MD surrogate model, and a relaxation of binary programming to solve the protein design task while optimizing both energy and the structural stability of proteins. We demonstrate the effectiveness of our design framework in designing proteins that can function under non-ambient conditions.

Application of computational methods for class A GPCR Ligand discovery

G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development due to their role in transmitting cellular signals in a multitude of biological processes. Of the six classes categorizing GPCR (A, B, C, D, E, and F), class A contains the largest number of therapeutically relevant GPCR. Despite their importance as drug targets, many challenges exist for the discovery of novel class A GPCR ligands serving as drug precursors. Though knowledge of the structural and functional characteristics of GPCR has grown significantly over the past 20 years, a large portion of GPCR lack reported, experimentally determined structures. Furthermore, many GPCR have no known endogenous and/or synthetic ligands, limiting further exploration of their biochemical, cellular, and physiological roles. While many successes in GPCR ligand discovery have resulted from experimental high-throughput screening, computational methods have played an increasingly important role in GPCR ligand identification in the past decade. Here we discuss computational techniques applied to GPCR ligand discovery. This review summarizes class A GPCR structure/function and provides an overview of many obstacles currently faced in GPCR ligand discovery. Furthermore, we discuss applications and recent successes of computational techniques used to predict GPCR structure as well as present a summary of ligand- and structure-based methods used to identify potential GPCR ligands. Finally, we discuss computational hit list generation and refinement and provide comprehensive workflows for GPCR ligand identification.