AlphaFold2 protein structure prediction: Implications for drug discovery

Investigations on genomic, topological and structural properties of diguanylate cyclases involved in Vibrio cholerae biofilm signalling using in silico techniques: Promising drug targets in combating cholera

During various stages of its life cycle, Vibrio cholerae initiate biofilm signalling cascade. Intercellular high level of the signalling nucleotide 3'-5' cyclic dimeric guanosine monophosphate (c-di-GMP), synthesized by diguanylate cyclases (DGCs) from its precursor molecule GTP, is crucial for biofilm formation. Present study endeavours to in silico approaches in evaluating genomic, physicochemical, topological and functional properties of six c-di-GMP regulatory DGCs (CdgA, CdgH, CdgK, CdgL, CdgM, VpvC) of V. cholerae. Genomic investigations unveiled that codon preferences were inclined towards AU ending over GC ending codons and overall GC content ranged from 44.6 to 49.5 with codon adaptation index ranging from 0.707 to 0.783. Topological analyses deciphered the presence of transmembrane domains in all proteins. All the DGCs were acidic, hydrophilic and thermostable. Only CdgA, CdgH and VpvC were predicted to be stable during in vitro conditions. Non-polar amino acids with leucine being the most abundant amino acid among these DGCs with α-helix as the predominant secondary structure, responsible for forming the transmembrane regions by secondary structure analysis. Tertiary structures of the proteins were obtained by computation using AlphaFold and trRosetta. Predicted structures by both the servers were compared in various aspects using PROCHECK, ERRAT and Modfold8 servers. Selected 3D structures were refined using GalaxyRefine. InterPro Scan revealed presence of a conserved GGDEF domain in all DGCs and predicted the active site residues in the GGDEF domain. Molecular docking studies using CB-DOCK 2 tool revealed that among the DGCs, VpvC exhibited highest affinity for GTP (-5.6 kcal/mol), which was closely followed by CdgL (-5.5 kcal/mol). MD simulations depicted all DGC-GTP complexes to be stable due to its considerably low eigenvalues. Such studies are considered to provide maiden insights into the genomic and structural properties of V. cholerae DGCs, actively involved in biofilm signalling systems, and it is projected to be beneficial in the discovery of novel DGC inhibitors that can target and downregulate the c-di-GMP regulatory system to develop anti-biofilm strategies against the cholera pathogen.

Role of artificial intelligence in cancer drug discovery and development

The role of artificial intelligence (AI) in cancer drug discovery and development has garnered significant attention due to its potential to transform the traditionally time-consuming and expensive processes involved in bringing new therapies to market. AI technologies, such as machine learning (ML) and deep learning (DL), enable the efficient analysis of vast datasets, facilitate faster identification of drug targets, optimization of compounds, and prediction of clinical outcomes. This review explores the multifaceted applications of AI across various stages of cancer drug development, from early-stage discovery to clinical trial design, development. In early-stage discovery, AI-driven methods support target identification, virtual screening (VS), and molecular docking, offering precise predictions that streamline the identification of promising compounds. Additionally, AI is instrumental in de novo drug design and lead optimization, where algorithms can generate novel molecular structures and optimize their properties to enhance drug efficacy and safety profiles. Preclinical development benefits from AI's predictive modeling capabilities, particularly in assessing a drug's toxicity through in silico simulations. AI also plays a pivotal role in biomarker discovery, enabling the identification of specific molecular signatures that can inform patient stratification and personalized treatment approaches. In clinical development, AI optimizes trial design by leveraging real-world data (RWD), improving patient selection, and reducing the time required to bring new drugs to market. Despite its transformative potential, challenges remain, including issues related to data quality, model interpretability, and regulatory hurdles. Addressing these limitations is critical for fully realizing AI's potential in cancer drug discovery and development. As AI continues to evolve, its integration with other technologies, such as genomics and clustered regularly interspaced short palindromic repeats (CRISPR), holds promise for advancing personalized cancer therapies. This review provides a comprehensive overview of AI's impact on the cancer drug discovery and development and highlights future directions for this rapidly evolving field.

Unveiling the influence of fastest nobel prize winner discovery: alphafold's algorithmic intelligence in medical sciences

CONTEXT

AlphaFold's advanced AI technology has transformed protein structure interpretation. By predicting three-dimensional protein structures from amino acid sequences, AlphaFold has solved the complex protein-folding problem, previously challenging for experimental methods due to numerous possible conformations. Since its inception, AlphaFold has introduced several versions, including AlphaFold2, AlphaFold DB, AlphaFold Multimer, Alpha Missense, and AlphaFold3, each further enhancing protein structure prediction. Remarkably, AlphaFold is recognized as the fastest Nobel Prize winner in science history. This technology has extensive applications, potentially transforming treatment and diagnosis in medical sciences by reducing drug design costs and time, while elucidating structural pathways of human body systems. Numerous studies have demonstrated how AlphaFold aids in understanding health conditions by providing critical information about protein mutations, abnormal protein-protein interactions, and changes in protein dynamics. Researchers have also developed new technologies and pipelines using different versions of AlphaFold to amplify its potential. However, addressing existing limitations is crucial to maximizing AlphaFold's capacity to redefine medical research. This article reviews AlphaFold's impact on five key aspects of medical sciences: protein mutation, protein-protein interaction, molecular dynamics, drug design, and immunotherapy.

METHODS

This review examines the contributions of various AlphaFold versions AlphaFold2, AlphaFold DB, AlphaFold Multimer, Alpha Missense, and AlphaFold3 to protein structure prediction. The methods include an extensive analysis of computational techniques and software used in interpreting and predicting protein structures, emphasizing advances in AI technology and its applications in medical research.

Integrating network pharmacology and experimental validation to advance psoriasis treatment: Multi-target mechanistic elucidation of medicinal herbs and natural compounds

BACKGROUND

Psoriasis, a chronic immune-mediated inflammatory disease (IMID), presents significant therapeutic challenges, necessitating exploration of alternative treatments like medicinal herbs (MH) and natural compounds (NC). Network pharmacology offers predictive insights, yet a systematic evaluation connecting these predictions with experimental validation outcomes specifically for MH/NC in psoriasis is lacking. This review specifically fills this gap by comprehensively integrating and analyzing studies that combine network pharmacology predictions with subsequent experimental validation.

METHODS

A systematic literature search identified 44 studies employing both network pharmacology and in vitro or in vivo experimental methods for MH/NC targeting psoriasis. This review provides a systematic analysis of the specific network pharmacology platforms, predicted targets/pathways, in vivo and in vitro experimental validation models, and key biomarker changes reported across these integrated studies. Methodological approaches and the consistency between predictions and empirical findings were critically evaluated.

RESULTS

This first comprehensive analysis reveals that network pharmacology predictions regarding MH/NC mechanisms in psoriasis are frequently corroborated by experimental data. Key signaling pathways, including the IL-17/IL-23 axis, MAPK, and NF-κB, emerge as consistently predicted and experimentally validated targets across diverse natural products. The review maps the specific network pharmacology tools and experimental designs utilized, establishing a methodological benchmark for the field and highlighting the successful synergy between computational prediction and empirical verification.

CONCLUSION

By systematically integrating and critically assessing the linkage between network pharmacology predictions and experimental validation for MH/NC in psoriasis, this review offers a unique clarification of the current, validated state-of-the-art, differentiating it from previous literature. It confirms network pharmacology's predictive power for natural products, identifies robustly validated therapeutic pathways, and provides a crucial benchmark, offering data-driven insights for future research into artificial intelligence-enhanced natural product-based therapies for psoriasis and other IMIDs.

Large Language Models in Genomics-A Perspective on Personalized Medicine

Integrating artificial intelligence (AI), particularly large language models (LLMs), into the healthcare industry is revolutionizing the field of medicine. LLMs possess the capability to analyze the scientific literature and genomic data by comprehending and producing human-like text. This enhances the accuracy, precision, and efficiency of extensive genomic analyses through contextualization. LLMs have made significant advancements in their ability to understand complex genetic terminology and accurately predict medical outcomes. These capabilities allow for a more thorough understanding of genetic influences on health issues and the creation of more effective therapies. This review emphasizes LLMs' significant impact on healthcare, evaluates their triumphs and limitations in genomic data processing, and makes recommendations for addressing these limitations in order to enhance the healthcare system. It explores the latest advancements in LLMs for genomic analysis, focusing on enhancing disease diagnosis and treatment accuracy by taking into account an individual's genetic composition. It also anticipates a future in which AI-driven genomic analysis is commonplace in clinical practice, suggesting potential research areas. To effectively leverage LLMs' potential in personalized medicine, it is vital to actively support innovation across multiple sectors, ensuring that AI developments directly contribute to healthcare solutions tailored to individual patients.

New horizons at the interface of artificial intelligence and translational cancer research

Artificial intelligence (AI) is increasingly being utilized in cancer research as a computational strategy for analyzing multiomics datasets. Advances in single-cell and spatial profiling technologies have contributed significantly to our understanding of tumor biology, and AI methodologies are now being applied to accelerate translational efforts, including target discovery, biomarker identification, patient stratification, and therapeutic response prediction. Despite these advancements, the integration of AI into clinical workflows remains limited, presenting both challenges and opportunities. This review discusses AI applications in multiomics analysis and translational oncology, emphasizing their role in advancing biological discoveries and informing clinical decision-making. Key areas of focus include cellular heterogeneity, tumor microenvironment interactions, and AI-aided diagnostics. Challenges such as reproducibility, interpretability of AI models, and clinical integration are explored, with attention to strategies for addressing these hurdles. Together, these developments underscore the potential of AI and multiomics to enhance precision oncology and contribute to advancements in cancer care.

Molecular Modelling in Bioactive Peptide Discovery and Characterisation

Molecular modelling is a vital tool in the discovery and characterisation of bioactive peptides, providing insights into their structural properties and interactions with biological targets. Many models predicting bioactive peptide function or structure rely on their intrinsic properties, including the influence of amino acid composition, sequence, and chain length, which impact stability, folding, aggregation, and target interaction. Homology modelling predicts peptide structures based on known templates. Peptide-protein interactions can be explored using molecular docking techniques, but there are challenges related to the inherent flexibility of peptides, which can be addressed by more computationally intensive approaches that consider their movement over time, called molecular dynamics (MD). Virtual screening of many peptides, usually against a single target, enables rapid identification of potential bioactive peptides from large libraries, typically using docking approaches. The integration of artificial intelligence (AI) has transformed peptide discovery by leveraging large amounts of data. AlphaFold is a general protein structure prediction tool based on deep learning that has greatly improved the predictions of peptide conformations and interactions, in addition to providing estimates of model accuracy at each residue which greatly guide interpretation. Peptide function and structure prediction are being further enhanced using Protein Language Models (PLMs), which are large deep-learning-derived statistical models that learn computer representations useful to identify fundamental patterns of proteins. Recent methodological developments are discussed in the context of canonical peptides, as well as those with modifications and cyclisations. In designing potential peptide therapeutics, the main outstanding challenge for these methods is the incorporation of diverse non-canonical amino acids and cyclisations.

Advancements in protein structure prediction: A comparative overview of AlphaFold and its derivatives

This review provides a comprehensive analysis of AlphaFold (AF) and its derivatives (AF2 and AF3) in protein structure prediction. These tools have revolutionized structural biology with their highly accurate predictions, driving progress in protein modeling, drug discovery, and the study of protein dynamics. Its exceptional accuracy has redefined our understanding of protein folding, which enables groundbreaking advancements in protein design, disease research and discusses future integration with experimental techniques. In addition, their achievement features, architectures, important case studies, and noteworthy effects in the field of biology and medicine were evaluated. In consideration of the fact that AF2 is a relatively recent innovation, it has already been taken into account in many studies that highlight its applications in many ways. Moreover, the limitations of AF2 that directed to the introduction of AF3 are also reported, which is a great improvement as it provides precise predictions of the structures and interactions of proteins, DNA, RNA, and ligands, thereby aiding in the understanding of the molecular level. Addressing current challenges and forecasting future developments, this work underscores the lasting significance of AF in reshaping the scientific landscape of protein research.

Characterization and application in recombinant N-GlcNAc-protein production of a novel endo-β-N-acetylglucosaminidase from Listeria booriae

Endo-β-N-acetylglucosaminidases (ENGases) are essential enzymes for hydrolyzing N-glycans, with applications in protein N-glycosylation analysis and glycoprotein synthesis. In this study, a novel GH18 family ENGase, Endo-LB, was identified from Listeria booriae FSL A5-0281. Composed of 593 amino acids (65.78 kDa), Endo-LB features with two domains: an Endo S-like catalytic domain and a mucin-binding protein (MucBP) domain. Recombinant Endo-LB, expressed in Escherichia coli BL21 (DE3) pLysS, exhibited a specific activity of 198.25 U/mg and hydrolyzed high mannose-type N-glycans at a temperature from 4 °C to 60 °C with optimal activity at 37 °C and pH 6.0 (range 3.0 to 10.0), making it versatile for various environmental conditions. The MucBP domain does not affect soluble Endo-LB activity but influences interaction with mucin on cell surface, suggesting potential application in targeting specific glycoproteins in complex biological environments. To address the heterogeneity of N-glycans in Pichia pastoris (Komagataella phaffii) expression, Endo-LB was further expressed in the Golgi of P. pastoris, efficiently producing glycoproteins, such as Erythropoietin (EPO) (37 mg/L) and Darbepoetin α (53 mg/L) with nearly complete N-glycans truncation, which can be further extended to generate diverse N-glycan structures. These findings highlight the versatility and potential utility of Endo-LB in glycoprotein engineering and biotechnological applications.

AlphaFold3 versus experimental structures: assessment of the accuracy in ligand-bound G protein-coupled receptors

G protein-coupled receptors (GPCRs) are critical drug targets involved in numerous physiological processes, yet many of their structures remain unresolved due to inherent flexibility and diverse ligand interactions. This study systematically evaluates the accuracy of AlphaFold3-predicted GPCR structures compared to experimentally determined structures, with a primary focus on ligand-bound states. Our analysis reveals that while AlphaFold3 shows improved performance over AlphaFold2 in predicting overall GPCR backbone architecture, significant discrepancies persist in ligand-binding poses, particularly for ions, peptides, and proteins. Despite advancements, these limitations constrain the utility of AlphaFold3 models in functional studies and structure-based drug design, where high-resolution details of ligand interactions are crucial. We assess the accuracy of predicted structures across various ligand types, quantifying deviations in binding pocket geometries and ligand orientations. Our findings highlight specific challenges in the computational prediction of ligand-bound GPCR structures, emphasizing areas where further refinement is needed. This study provides valuable insights for researchers using AlphaFold3 in GPCR studies, underscores the ongoing necessity for experimental structure determination, and offers direction for improving protein-ligand interaction predictions in future computational models.

An outlook on structural biology after AlphaFold: tools, limits and perspectives

AlphaFold and similar groundbreaking, AI-based tools, have revolutionized the field of structural bioinformatics, with their remarkable accuracy in ab-initio protein structure prediction. This success has catalyzed the development of new software and pipelines aimed at incorporating AlphaFold's predictions, often focusing on addressing the algorithm's remaining challenges. Here, we present the current landscape of structural bioinformatics shaped by AlphaFold, and discuss how the field is dynamically responding to this revolution, with new software, methods, and pipelines. While the excitement around AI-based tools led to their widespread application, it is essential to acknowledge that their practical success hinges on their integration into established protocols within structural bioinformatics, often neglected in the context of AI-driven advancements. Indeed, user-driven intervention is still as pivotal in the structure prediction process as in complementing state-of-the-art algorithms with functional and biological knowledge.

Key site residues of Cnaphalocrocis medinalis odorant-binding protein 13 CmedOBP13 involved in interacting with rice plant volatiles

Odorant binding proteins (OBPs) play key roles in the insect olfactory system by assisting the neuronal response to hydrophobic odor molecules, understanding their interaction with ligands will facilitate the virtual screening of behaviorally active compounds in insects. Here, we successfully cloned and confirmed CmedOBP13, an antennae-biased OBP from the rice leaffolder Cnaphalocrocis medinalis, as a secreted protein. Recombinant CmedOBP13 was obtained using the Escherichia coli system, and its binding affinities to 35 volatile compounds emitted by rice plants and three sex pheromone components from female moths were assessed by a competitive binding assay. The results revealed that CmedOBP13 exhibited binding affinity to 23 rice volatiles, while no binding affinity for sex pheromone components. Furthermore, the stability of its conformation was found to be dependent on the pH level. Finally, the interaction between CmedOBP13 and odorants was predicted and confirmed by molecular docking and mutation functional assays, respectively. The combination of multiple hydrophobic residues created an adequate hydrophobic setting for ligands, and three residues (Glu13, Arg34, and Tyr115) might form hydrogen bonds with 15 odorants. Single mutations of Glu13, Arg34, Leu72, and Tyr115 diminished the binding affinities of CmedOBP13 to corresponding odorants, respectively. These findings provided valuable insights into the mode of action of CmedOBP13 interacting with the volatiles of rice plants and will guide the screening of behaviorally active compounds against C. medinalis in future.

Challenges in bridging the gap between protein structure prediction and functional interpretation

The rapid evolution of protein structure prediction tools has significantly broadened access to protein structural data. Although predicted structure models have the potential to accelerate and impact fundamental and translational research significantly, it is essential to note that they are not validated and cannot be considered the ground truth. Thus, challenges persist, particularly in capturing protein dynamics, predicting multi-chain structures, interpreting protein function, and assessing model quality. Interdisciplinary collaborations are crucial to overcoming these obstacles. Databases like the AlphaFold Protein Structure Database, the ESM Metagenomic Atlas, and initiatives like the 3D-Beacons Network provide FAIR access to these data, enabling their interpretation and application across a broader scientific community. Whilst substantial advancements have been made in protein structure prediction, further progress is required to address the remaining challenges. Developing training materials, nurturing collaborations, and ensuring open data sharing will be paramount in this pursuit. The continued evolution of these tools and methodologies will deepen our understanding of protein function and accelerate disease pathogenesis and drug development discoveries.

Discovery and Characterization of Two Selective Inhibitors for a Mu-Class Glutathione S-Transferase of 25 kDa from Taenia solium Using Computational and Bioinformatics Tools

Glutathione S-transferases (GSTs) are promising pharmacological targets for developing antiparasitic agents against helminths, as they play a key role in detoxifying cytotoxic xenobiotics and managing oxidative stress. Inhibiting GST activity can compromise parasite viability. This study reports the successful identification of two selective inhibitors for the mu-class glutathione S-transferase of 25 kDa (Ts25GST) from Taenia solium, named i11 and i15, using a computationally guided approach. The workflow involved modeling and refining the 3D structure from the sequence using the AlphaFold algorithm and all-atom molecular dynamics simulations with an explicit solvent. Representative structures from these simulations and a putative binding site with low conservation relative to human GSTs, identified via the SILCS methodology, were employed for virtual screening through ensemble docking against a commercial compound library. The two compounds were found to reduce the enzyme's activity by 50-70% under assay conditions, while showing a reduction of only 30-35% for human mu-class GSTM1, demonstrating selectivity for Ts25GST. Notable, i11 displayed competitive inhibition with CDNB, while i15 exhibited a non-competitive inhibition type.

Revisiting the Plasmodium falciparum druggable genome using predicted structures and data mining

The identification of novel drug targets for the purpose of designing small molecule inhibitors is key component to modern drug discovery. In malaria parasites, discoveries of antimalarial targets have primarily occurred retroactively by investigating the mode of action of compounds found through phenotypic screens. Although this method has yielded many promising candidates, it is time- and resource-consuming and misses targets not captured by existing antimalarial compound libraries and phenotypic assay conditions. Leveraging recent advances in protein structure prediction and data mining, we systematically assessed the Plasmodium falciparum genome for proteins amenable to target-based drug discovery, identifying 867 candidate targets with evidence of small molecule binding and blood stage essentiality. Of these, 540 proteins showed strong essentiality evidence and lack inhibitors that have progressed to clinical trials. Expert review and rubric-based scoring of this subset based on additional criteria such as selectivity, structural information, and assay developability yielded 67 high priority candidates. This study also provides a genome-wide data resource and implements a generalizable framework for systematically evaluating and prioritizing novel pathogenic disease targets.

Exploring structural diversity across the protein universe with The Encyclopedia of Domains

The AlphaFold Protein Structure Database (AFDB) contains more than 214 million predicted protein structures composed of domains, which are independently folding units found in multiple structural and functional contexts. Identifying domains can enable many functional and evolutionary analyses but has remained challenging because of the sheer scale of the data. Using deep learning methods, we have detected and classified every domain in the AFDB, producing The Encyclopedia of Domains. We detected nearly 365 million domains, over 100 million more than can be found by sequence methods, covering more than 1 million taxa. Reassuringly, 77% of the nonredundant domains are similar to known superfamilies, greatly expanding representation of their domain space. We uncovered more than 10,000 new structural interactions between superfamilies and thousands of new folds across the fold space continuum.

Cognitive Impact of Neurotropic Pathogens: Investigating Molecular Mimicry through Computational Methods

Neurotropic pathogens, notably, herpesviruses, have been associated with significant neuropsychiatric effects. As a group, these pathogens can exploit molecular mimicry mechanisms to manipulate the host central nervous system to their advantage. Here, we present a systematic computational approach that may ultimately be used to unravel protein-protein interactions and molecular mimicry processes that have not yet been solved experimentally. Toward this end, we validate this approach by replicating a set of pre-existing experimental findings that document the structural and functional similarities shared by the human cytomegalovirus-encoded UL144 glycoprotein and human tumor necrosis factor receptor superfamily member 14 (TNFRSF14). We began with a thorough exploration of the Homo sapiens protein database using the Basic Local Alignment Search Tool (BLASTx) to identify proteins sharing sequence homology with UL144. Subsequently, we used AlphaFold2 to predict the independent three-dimensional structures of UL144 and TNFRSF14. This was followed by a comprehensive structural comparison facilitated by Distance-Matrix Alignment and Foldseek. Finally, we used AlphaFold-multimer and PPIscreenML to elucidate potential protein complexes and confirm the predicted binding activities of both UL144 and TNFRSF14. We then used our in silico approach to replicate the experimental finding that revealed TNFRSF14 binding to both B- and T-lymphocyte attenuator (BTLA) and glycoprotein domain and UL144 binding to BTLA alone. This computational framework offers promise in identifying structural similarities and interactions between pathogen-encoded proteins and their host counterparts. This information will provide valuable insights into the cognitive mechanisms underlying the neuropsychiatric effects of viral infections.

Modern microbiology: Embracing complexity through integration across scales

Microbes were the only form of life on Earth for most of its history, and they still account for the vast majority of life's diversity. They convert rocks to soil, produce much of the oxygen we breathe, remediate our sewage, and sustain agriculture. Microbes are vital to planetary health as they maintain biogeochemical cycles that produce and consume major greenhouse gases and support large food webs. Modern microbiologists analyze nucleic acids, proteins, and metabolites; leverage sophisticated genetic tools, software, and bioinformatic algorithms; and process and integrate complex and heterogeneous datasets so that microbial systems may be harnessed to address contemporary challenges in health, the environment, and basic science. Here, we consider an inevitably incomplete list of emergent themes in our discipline and highlight those that we recognize as the archetypes of its modern era that aim to address the most pressing problems of the 21st century.

A comprehensive review of artificial intelligence for pharmacology research

With the innovation and advancement of artificial intelligence, more and more artificial intelligence techniques are employed in drug research, biomedical frontier research, and clinical medicine practice, especially, in the field of pharmacology research. Thus, this review focuses on the applications of artificial intelligence in drug discovery, compound pharmacokinetic prediction, and clinical pharmacology. We briefly introduced the basic knowledge and development of artificial intelligence, presented a comprehensive review, and then summarized the latest studies and discussed the strengths and limitations of artificial intelligence models. Additionally, we highlighted several important studies and pointed out possible research directions.

GTalign: spatial index-driven protein structure alignment, superposition, and search

With protein databases growing rapidly due to advances in structural and computational biology, the ability to accurately align and rapidly search protein structures has become essential for biological research. In response to the challenge posed by vast protein structure repositories, GTalign offers an innovative solution to protein structure alignment and search-an algorithm that achieves optimal superposition at high speeds. Through the design and implementation of spatial structure indexing, GTalign parallelizes all stages of superposition search across residues and protein structure pairs, yielding rapid identification of optimal superpositions. Rigorous evaluation across diverse datasets reveals GTalign as the most accurate among structure aligners while presenting orders of magnitude in speedup at state-of-the-art accuracy. GTalign's high speed and accuracy make it useful for numerous applications, including functional inference, evolutionary analyses, protein design, and drug discovery, contributing to advancing understanding of protein structure and function.

Identification of flavor peptides based on virtual screening and molecular docking from Hypsizygus marmoreuss

Hypsizygus marmoreuss is an under-explored source of flavor peptides that can enhance the flavor of NaCl or MSG, allowing products to be reformulated in line with reduction policies. This study utilized advanced techniques, including UPLC-Q-TOF MS/MS and molecular docking, to identify H. marmoreuss peptides. Sensory evaluations revealed 10 peptides with pronounced umami flavors and seven with dominantly salty tastes. VLPVPQK scored highest for umami intensity (5.2), and EGNPAHQK for salty intensity (6.2). Further investigation influenced by 0.35 % MSG or 0.35 % NaCl exposed peptides with elevated umami and salty thresholds. LDSPATPEK, VVEGEPSLK, and QKLPEKPER had umami-enhancing thresholds of 0.18, 0.18, and 0.35 mM, while LDSPATPEK and VVEGEPSLK had similar thresholds for salt (0.09 mM). Molecular docking revealed that taste receptor proteins interacted with umami peptides through hydrogen, carbon-hydrogen, alkyl, and van der Waals forces. Specific amino acids in the umami receptor T1R1 had roles in bonding with umami peptides through hydrogen and carbon-hydrogen interactions. In conclusion, molecular docking proved to be an effective and efficient method for flavor peptide screening. Further, this study demonstrated that flavor peptides from H. marmoreuss had the capacity to enhance NaCl and MSG flavours and might be useful tools for reformulation, reducing salt and MSG contents.

The Role of AI in Drug Discovery

The emergence of Artificial Intelligence (AI) in drug discovery marks a pivotal shift in pharmaceutical research, blending sophisticated computational techniques with conventional scientific exploration to break through enduring obstacles. This review paper elucidates the multifaceted applications of AI across various stages of drug development, highlighting significant advancements and methodologies. It delves into AI's instrumental role in drug design, polypharmacology, chemical synthesis, drug repurposing, and the prediction of drug properties such as toxicity, bioactivity, and physicochemical characteristics. Despite AI's promising advancements, the paper also addresses the challenges and limitations encountered in the field, including data quality, generalizability, computational demands, and ethical considerations. By offering a comprehensive overview of AI's role in drug discovery, this paper underscores the technology's potential to significantly enhance drug development, while also acknowledging the hurdles that must be overcome to fully realize its benefits.

Review of AlphaFold 3: Transformative Advances in Drug Design and Therapeutics

Google DeepMind Technologies Limited (London, United Kingdom) recently released its new version of the biomolecular structure predictor artificial intelligence (AI) model named AlphaFold 3. Superior in accuracy and more powerful than its predecessor AlphaFold 2, this innovation has astonished the world with its capacity and speed. It takes humans years to determine the structure of various proteins and how the shape works with the receptors but AlphaFold 3 predicts the same structure in seconds. The version's utility is unimaginable in the field of drug discoveries, vaccines, enzymatic processes, and determining the rate and effect of different biological processes. AlphaFold 3 uses similar machine learning and deep learning models such as Gemini (Google DeepMind Technologies Limited). AlphaFold 3 has already established itself as a turning point in the field of computational biochemistry and drug development along with receptor modulation and biomolecular development. With the help of AlphaFold 3 and models similar to this, researchers will gain unparalleled insights into the structural dynamics of proteins and their interactions, opening up new avenues for scientists and doctors to exploit for the benefit of the patient. The integration of AI models like AlphaFold 3, bolstered by rigorous validation against high-standard research publications, is set to catalyze further innovations and offer a glimpse into the future of biomedicine.

Comprehensive functional characterization of complement factor I rare variant genotypes identified in the SCOPE geographic atrophy cohort

Rare variants (RVs) in the gene encoding the regulatory enzyme complement factor I (CFI; FI) that reduce protein function or levels increase age-related macular degeneration risk. A total of 3357 subjects underwent screening in the SCOPE natural history study for geographic atrophy secondary to age-related macular degeneration, including CFI sequencing and serum FI measurement. Eleven CFI RV genotypes that were challenging to categorize as type I (low serum level) or type II (normal serum level, reduced enzymatic function) were characterized in the context of pure FI protein in C3b and C4b fluid phase cleavage assays and a novel bead-based functional assay (BBFA) of C3b cleavage. Four variants predicted or previously characterized as benign were analyzed by BBFA for comparison. In all, three variants (W51S, C67R, and I370T) resulted in low expression. Furthermore, four variants (P64L, R339Q, G527V, and P528T) were identified as being highly deleterious with IC50s for C3b breakdown >1 log increased versus the WT protein, while two variants (K476E and R474Q) were ∼1 log reduced in function. Meanwhile, six variants (P50A, T203I, K441R, E548Q, P553S, and S570T) had IC50s similar to WT. Odds ratios and BBFA IC50s were positively correlated (r = 0.76, p < 0.01), while odds ratios versus combined annotation dependent depletion (CADD) scores were not (r = 0.43, p = 0.16). Overall, 15 CFI RVs were functionally characterized which may aid future patient stratification for complement-targeted therapies. Pure protein in vitro analysis remains the gold standard for determining the functional consequence of CFI RVs.

Towards evidence-based practice 2.0: leveraging artificial intelligence in healthcare

Background

Evidence-based practice (EBP) involves making clinical decisions based on three sources of information: evidence, clinical experience and patient preferences. Despite popularization of EBP, research has shown that there are many barriers to achieving the goals of the EBP model. The use of artificial intelligence (AI) in healthcare has been proposed as a means to improve clinical decision-making. The aim of this paper was to pinpoint key challenges pertaining to the three pillars of EBP and to investigate the potential of AI in surmounting these challenges and contributing to a more evidence-based healthcare practice. We conducted a selective review of the literature on EBP and the integration of AI in healthcare to achieve this.

Challenges with the three components of EBP

Clinical decision-making in line with the EBP model presents several challenges. The availability and existence of robust evidence sometimes pose limitations due to slow generation and dissemination processes, as well as the scarcity of high-quality evidence. Direct application of evidence is not always viable because studies often involve patient groups distinct from those encountered in routine healthcare. Clinicians need to rely on their clinical experience to interpret the relevance of evidence and contextualize it within the unique needs of their patients. Moreover, clinical decision-making might be influenced by cognitive and implicit biases. Achieving patient involvement and shared decision-making between clinicians and patients remains challenging in routine healthcare practice due to factors such as low levels of health literacy among patients and their reluctance to actively participate, barriers rooted in clinicians' attitudes, scepticism towards patient knowledge and ineffective communication strategies, busy healthcare environments and limited resources.

AI assistance for the three components of EBP

AI presents a promising solution to address several challenges inherent in the research process, from conducting studies, generating evidence, synthesizing findings, and disseminating crucial information to clinicians to implementing these findings into routine practice. AI systems have a distinct advantage over human clinicians in processing specific types of data and information. The use of AI has shown great promise in areas such as image analysis. AI presents promising avenues to enhance patient engagement by saving time for clinicians and has the potential to increase patient autonomy although there is a lack of research on this issue.

Conclusion

This review underscores AI's potential to augment evidence-based healthcare practices, potentially marking the emergence of EBP 2.0. However, there are also uncertainties regarding how AI will contribute to a more evidence-based healthcare. Hence, empirical research is essential to validate and substantiate various aspects of AI use in healthcare.

Cracking AlphaFold2: Leveraging the power of artificial intelligence in undergraduate biochemistry curriculums

AlphaFold2 is an Artificial Intelligence-based program developed to predict the 3D structure of proteins given only their amino acid sequence at atomic resolution. Due to the accuracy and efficiency at which AlphaFold2 can generate 3D structure predictions and its widespread adoption into various aspects of biochemical research, the technique of protein structure prediction should be considered for incorporation into the undergraduate biochemistry curriculum. A module for introducing AlphaFold2 into a senior-level biochemistry laboratory classroom was developed. The module's focus was to have students predict the structures of proteins from the MPOX 22 global outbreak virus isolate genome, which had no structures elucidated at that time. The goal of this study was to both determine the impact the module had on students and to develop a framework for introducing AlphaFold2 into the undergraduate curriculum so that instructors for biochemistry courses, regardless of their background in bioinformatics, could adapt the module into their classrooms.

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.

Current perspectives and trend of computer-aided drug design: a review and bibliometric analysis

AIM

Computer-aided drug design (CADD) is a drug design technique for computing ligand-receptor interactions and is involved in various stages of drug development. To better grasp the frontiers and hotspots of CADD, we conducted a review analysis through bibliometrics.

METHODS

A systematic review of studies published between 2000 and 20 July 2023 was conducted following the PRISMA guidelines. Literature on CADD was selected from the Web of Science Core Collection. General information, publications, output trends, countries/regions, institutions, journals, keywords, and influential authors were visually analyzed using software such as Excel, VOSviewer, RStudio, and CiteSpace.

RESULTS

A total of 2031 publications were included. These publications primarily originated from 99 countries or regions led by the U.S. and China. Among the contributors, MacKerell AD had the highest number of articles and the greatest influence. The Journal of Medicinal Chemistry was the most cited journal, whereas the Journal of Chemical Information and Modeling had the highest number of publications.

CONCLUSIONS

Influential authors in the field were identified. Current research shows active collaboration between countries, institutions, and companies. CADD technologies such as homology modeling, pharmacophore modeling, quantitative conformational relationships, molecular docking, molecular dynamics simulation, binding free energy prediction, and high-throughput virtual screening can effectively improve the efficiency of new drug discovery. Artificial intelligence-assisted drug design and screening based on CADD represent key topics that will influence future development. Furthermore, this paper will be helpful in better understanding the frontiers and hotspots of CADD.

Petabase-Scale Homology Search for Structure Prediction

The recent CASP15 competition highlighted the critical role of multiple sequence alignments (MSAs) in protein structure prediction, as demonstrated by the success of the top AlphaFold2-based prediction methods. To push the boundaries of MSA utilization, we conducted a petabase-scale search of the Sequence Read Archive (SRA), resulting in gigabytes of aligned homologs for CASP15 targets. These were merged with default MSAs produced by ColabFold-search and provided to ColabFold-predict. By using SRA data, we achieved highly accurate predictions (GDT_TS > 70) for 66% of the non-easy targets, whereas using ColabFold-search default MSAs scored highly in only 52%. Next, we tested the effect of deep homology search and ColabFold's advanced features, such as more recycles, on prediction accuracy. While SRA homologs were most significant for improving ColabFold's CASP15 ranking from 11th to 3rd place, other strategies contributed too. We analyze these in the context of existing strategies to improve prediction.

Application of artificial intelligence and machine learning in drug repurposing

The purpose of drug repurposing is to leverage previously approved drugs for a particular disease indication and apply them to another disease. It can be seen as a faster and more cost-effective approach to drug discovery and a powerful tool for achieving precision medicine. In addition, drug repurposing can be used to identify therapeutic candidates for rare diseases and phenotypic conditions with limited information on disease biology. Machine learning and artificial intelligence (AI) methodologies have enabled the construction of effective, data-driven repurposing pipelines by integrating and analyzing large-scale biomedical data. Recent technological advances, especially in heterogeneous network mining and natural language processing, have opened up exciting new opportunities and analytical strategies for drug repurposing. In this review, we first introduce the challenges in repurposing approaches and highlight some success stories, including those during the COVID-19 pandemic. Next, we review some existing computational frameworks in the literature, organized on the basis of the type of biomedical input data analyzed and the computational algorithms involved. In conclusion, we outline some exciting new directions that drug repurposing research may take, as pioneered by the generative AI revolution.

High-throughput prediction of protein conformational distributions with subsampled AlphaFold2

This paper presents an innovative approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict proteins' ground state conformations and is limited in its ability to predict conformational landscapes. Here, we demonstrate how AlphaFold 2 can directly predict the relative populations of different protein conformations by subsampling multiple sequence alignments. We tested our method against nuclear magnetic resonance experiments on two proteins with drastically different amounts of available sequence data, Abl1 kinase and the granulocyte-macrophage colony-stimulating factor, and predicted changes in their relative state populations with more than 80% accuracy. Our subsampling approach worked best when used to qualitatively predict the effects of mutations or evolution on the conformational landscape and well-populated states of proteins. It thus offers a fast and cost-effective way to predict the relative populations of protein conformations at even single-point mutation resolution, making it a useful tool for pharmacology, analysis of experimental results, and predicting evolution.

Deep learning in structural bioinformatics: current applications and future perspectives

In this review article, we explore the transformative impact of deep learning (DL) on structural bioinformatics, emphasizing its pivotal role in a scientific revolution driven by extensive data, accessible toolkits and robust computing resources. As big data continue to advance, DL is poised to become an integral component in healthcare and biology, revolutionizing analytical processes. Our comprehensive review provides detailed insights into DL, featuring specific demonstrations of its notable applications in bioinformatics. We address challenges tailored for DL, spotlight recent successes in structural bioinformatics and present a clear exposition of DL-from basic shallow neural networks to advanced models such as convolution, recurrent, artificial and transformer neural networks. This paper discusses the emerging use of DL for understanding biomolecular structures, anticipating ongoing developments and applications in the realm of structural bioinformatics.

Advances in AI for Protein Structure Prediction: Implications for Cancer Drug Discovery and Development

Recent advancements in AI-driven technologies, particularly in protein structure prediction, are significantly reshaping the landscape of drug discovery and development. This review focuses on the question of how these technological breakthroughs, exemplified by AlphaFold2, are revolutionizing our understanding of protein structure and function changes underlying cancer and improve our approaches to counter them. By enhancing the precision and speed at which drug targets are identified and drug candidates can be designed and optimized, these technologies are streamlining the entire drug development process. We explore the use of AlphaFold2 in cancer drug development, scrutinizing its efficacy, limitations, and potential challenges. We also compare AlphaFold2 with other algorithms like ESMFold, explaining the diverse methodologies employed in this field and the practical effects of these differences for the application of specific algorithms. Additionally, we discuss the broader applications of these technologies, including the prediction of protein complex structures and the generative AI-driven design of novel proteins.

Computational studies on rep and capsid proteins of CRESS DNA viruses

The circular rep-encoding single-stranded DNA viruses (CRESS DNA viruses) are among the smallest, with 2-6 kb ssDNA genomes that encode for a coat protein (C) and a replication protein (R). To comprehend the complexity and divergence of the C and R proteins, we have created predictive structural models of representative viruses infecting unique hosts from each family using the neural network-based method AlphaFold2 and carried out molecular dynamic simulations to assess their stability. The structural characteristics indicate that differences in loops and amino-terminus may play a significant role in facilitating adaptations to multiple hosts and vectors. In comparison to the C, the Rs show a high degree of conservation and structural mimicry of the nuclease-helicase domains of plasmids. A phylogenetic analysis based on the structures and sequences of the C and R proteins reveals evolutionary variances. Our study also highlights the conservation of structural components involved in the interaction of R with the conserved intergenic region of the genome. Further, we envisage that the adaptability of R's central linker may be crucial for establishing interactions with multiple protein partners, including C.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13337-024-00858-x.

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

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

Structural Insight into Geranylgeranyl Diphosphate Synthase (GGDPS) for Cancer Therapy

Geranylgeranyl diphosphate synthase (GGDPS), the source of the isoprenoid donor in protein geranylgeranylation reactions, has become an attractive target for anticancer therapy due to the reliance of cancers on geranylgeranylated proteins. Current GGDPS inhibitor development focuses on optimizing the drug-target enzyme interactions of nitrogen-containing bisphosphonate-based drugs. To advance GGDPS inhibitor development, understanding the enzyme structure, active site, and ligand/product interactions is essential. Here we provide a comprehensive structure-focused review of GGDPS. We reviewed available yeast and human GGDPS structures and then used AlphaFold modeling to complete unsolved structural aspects of these models. We delineate the elements of higher-order structure formation, product-substrate binding, the electrostatic surface, and small-molecule inhibitor binding. With the rise of structure-based drug design, the information provided here will serve as a valuable tool for rationally optimizing inhibitor selectivity and effectiveness.

Predictive Modeling of Proteins Encoded by a Plant Virus Sheds a New Light on Their Structure and Inherent Multifunctionality

Plant virus genomes encode proteins that are involved in replication, encapsidation, cell-to-cell, and long-distance movement, avoidance of host detection, counter-defense, and transmission from host to host, among other functions. Even though the multifunctionality of plant viral proteins is well documented, contemporary functional repertoires of individual proteins are incomplete. However, these can be enhanced by modeling tools. Here, predictive modeling of proteins encoded by the two genomic RNAs, i.e., RNA1 and RNA2, of grapevine fanleaf virus (GFLV) and their satellite RNAs by a suite of protein prediction software confirmed not only previously validated functions (suppressor of RNA silencing [VSR], viral genome-linked protein [VPg], protease [Pro], symptom determinant [Sd], homing protein [HP], movement protein [MP], coat protein [CP], and transmission determinant [Td]) and previously identified putative functions (helicase [Hel] and RNA-dependent RNA polymerase [Pol]), but also predicted novel functions with varying levels of confidence. These include a T3/T7-like RNA polymerase domain for protein 1AVSR, a short-chain reductase for protein 1BHel/VSR, a parathyroid hormone family domain for protein 1EPol/Sd, overlapping domains of unknown function and an ABC transporter domain for protein 2BMP, and DNA topoisomerase domains, transcription factor FBXO25 domain, or DNA Pol subunit cdc27 domain for the satellite RNA protein. Structural predictions for proteins 2AHP/Sd, 2BMP, and 3A? had low confidence, while predictions for proteins 1AVSR, 1BHel*/VSR, 1CVPg, 1DPro, 1EPol*/Sd, and 2CCP/Td retained higher confidence in at least one prediction. This research provided new insights into the structure and functions of GFLV proteins and their satellite protein. Future work is needed to validate these findings.

High-Throughput Structure-Based Drug Design (HT-SBDD) Using Drug Docking, Fragment Molecular Orbital Calculations, and Molecular Dynamic Techniques

Structure-based drug design (SBDD) is rapidly evolving to be a fundamental tool for faster and more cost-effective methods of lead drug discovery. SBDD aims to offer a computational replacement to traditional high-throughput screening (HTS) methods of drug discovery. This "virtual screening" technique utilizes the structural data of a target protein in conjunction with large databases of potential drug candidates and then applies a range of different computational techniques to determine which potential candidates are likely to bind with high affinity and efficacy. It is proposed that high-throughput SBDD (HT-SBDD) will significantly enrich the success rate of HTS methods, which currently fluctuates around ~1%. In this chapter, we focus on the theory and utility of high-throughput drug docking, fragment molecular orbital calculations, and molecular dynamics techniques. We also offer a comparative review of the benefits and limitations of traditional methods against more recent SBDD advances. As HT-SBDD is computationally intensive, we will also cover the important role high-performance computing (HPC) clusters play in the future of computational drug discovery.

AlphaFold2 for Protein Structure Prediction: Best Practices and Critical Analyses

AlphaFold2 (AF2) has emerged in recent years as a groundbreaking innovation that has revolutionized several scientific fields, in particular structural biology, drug design, and the elucidation of disease mechanisms. Many scientists now use AF2 on a daily basis, including non-specialist users. This chapter is aimed at the latter. Tips and tricks for getting the most out of AF2 to produce a high-quality biological model are discussed here. We suggest to non-specialist users how to maintain a critical perspective when working with AF2 models and provide guidelines on how to properly evaluate them. After showing how to perform our own structure prediction using ColabFold, we list several ways to improve AF2 models by adding information that is missing from the original AF2 model. By using software such as AlphaFill to add cofactors and ligands to the models, or MODELLER to add disulfide bridges between cysteines, we guide users to build a high-quality biological model suitable for applications such as drug design, protein interaction, or molecular dynamics studies.

Predicting Relative Populations of Protein Conformations without a Physics Engine Using AlphaFold 2

This paper presents a novel approach for predicting the relative populations of protein conformations using AlphaFold 2, an AI-powered method that has revolutionized biology by enabling the accurate prediction of protein structures. While AlphaFold 2 has shown exceptional accuracy and speed, it is designed to predict proteins' ground state conformations and is limited in its ability to predict conformational landscapes. Here, we demonstrate how AlphaFold 2 can directly predict the relative populations of different protein conformations by subsampling multiple sequence alignments. We tested our method against NMR experiments on two proteins with drastically different amounts of available sequence data, Abl1 kinase and the granulocyte-macrophage colony-stimulating factor, and predicted changes in their relative state populations with more than 80% accuracy. Our subsampling approach worked best when used to qualitatively predict the effects of mutations or evolution on the conformational landscape and well-populated states of proteins. It thus offers a fast and cost-effective way to predict the relative populations of protein conformations at even single-point mutation resolution, making it a useful tool for pharmacology, NMR analysis, and evolution.

Development of anti-PD-L1 antibody based on structure prediction of AlphaFold2

Accurate structural information plays a crucial role in comprehending biological processes and designing drugs. Indeed, the remarkable precision of the AlphaFold2 has facilitated significant advancements in predicting molecular structures, encompassing antibodies and antigens. This breakthrough has paved the way for rational drug design, ushering in new possibilities in the field of pharmaceutical development. Within this study, performing analysis and humanization guided by the structures predicted by AlphaFold2. Notably, the resulting humanized antibody, h3D5-hIgG1, demonstrated exceptional binding affinity to the PD-L1 protein. The KD value of parental antibody 3D5-hIgG1 was increased by nearly 7 times after humanization. Both h3D5-hIgG1 and 3D5-hIgG1 bound to cells expressing human PD-L1 with EC50 values of 5.13 and 9.92nM, respectively. Humanization resulted in a twofold increase in the binding capacity of the antibody, with h3D5-hIgG1 exhibiting superior performance compared to the parental antibody 3D5-hIgG1. Furthermore, h3D5-hIgG1 promoted cytokine secretion of T cells, and significantly suppressed MC38-hPD-L1 tumor growth. This study highlights the potential for artificial intelligence-assisted drug development, which is poised to become a prominent trend in the future.

Precision Oncology Comes of Age: Designing Best-in-Class Small Molecules by Integrating Two Decades of Advances in Chemistry, Target Biology, and Data Science

Small-molecule drugs have enabled the practice of precision oncology for genetically defined patient populations since the first approval of imatinib in 2001. Scientific and technology advances over this 20-year period have driven the evolution of cancer biology, medicinal chemistry, and data science. Collectively, these advances provide tools to more consistently design best-in-class small-molecule drugs against known, previously undruggable, and novel cancer targets. The integration of these tools and their customization in the hands of skilled drug hunters will be necessary to enable the discovery of transformational therapies for patients across a wider spectrum of cancers.

SIGNIFICANCE

Target-centric small-molecule drug discovery necessitates the consideration of multiple approaches to identify chemical matter that can be optimized into drug candidates. To do this successfully and consistently, drug hunters require a comprehensive toolbox to avoid following the "law of instrument" or Maslow's hammer concept where only one tool is applied regardless of the requirements of the task. Combining our ever-increasing understanding of cancer and cancer targets with the technological advances in drug discovery described below will accelerate the next generation of small-molecule drugs in oncology.

Advancements in small molecule drug design: A structural perspective

In this review, we outline recent advancements in small molecule drug design from a structural perspective. We compare protein structure prediction methods and explore the role of the ligand binding pocket in structure-based drug design. We examine various structural features used to optimize drug candidates, including functional groups, stereochemistry, and molecular weight. Computational tools such as molecular docking and virtual screening are discussed for predicting and optimizing drug candidate structures. We present examples of drug candidates designed based on their molecular structure and discuss future directions in the field. By effectively integrating structural information with other valuable data sources, we can improve the drug discovery process, leading to the identification of novel therapeutics with improved efficacy, specificity, and safety profiles.

Identification of histidine kinase inhibitors through screening of natural compounds to combat mastitis caused by Streptococcus agalactiae in dairy cattle

BACKGROUND

Mastitis poses a major threat to dairy farms globally; it results in reduced milk production, increased treatment costs, untimely compromised genetic potential, animal deaths, and economic losses. Streptococcus agalactiae is a highly virulent bacteria that cause mastitis. The administration of antibiotics for the treatment of this infection is not advised due to concerns about the emergence of antibiotic resistance and potential adverse effects on human health. Thus, there is a critical need to identify new therapeutic approaches to combat mastitis. One promising target for the development of antibacterial therapies is the transmembrane histidine kinase of bacteria, which plays a key role in signal transduction pathways, secretion systems, virulence, and antibiotic resistance.

RESULTS

In this study, we aimed to identify novel natural compounds that can inhibit transmembrane histidine kinase. To achieve this goal, we conducted a virtual screening of 224,205 natural compounds, selecting the top ten based on their lowest binding energy and favorable protein-ligand interactions. Furthermore, molecular docking of eight selected antibiotics and five histidine kinase inhibitors with transmembrane histidine kinase was performed to evaluate the binding energy with respect to top-screened natural compounds. We also analyzed the ADMET properties of these compounds to assess their drug-likeness. The top two compounds (ZINC000085569031 and ZINC000257435291) and top-screened antibiotics (Tetracycline) that demonstrated a strong binding affinity were subjected to molecular dynamics simulations (100 ns), free energy landscape, and binding free energy calculations using the MM-PBSA method.

CONCLUSION

Our results suggest that the selected natural compounds have the potential to serve as effective inhibitors of transmembrane histidine kinase and can be utilized for the development of novel antibacterial veterinary medicine for mastitis after further validation through clinical studies.

Petascale Homology Search for Structure Prediction

The recent CASP15 competition highlighted the critical role of multiple sequence alignments (MSAs) in protein structure prediction, as demonstrated by the success of the top AlphaFold2-based prediction methods. To push the boundaries of MSA utilization, we conducted a petabase-scale search of the Sequence Read Archive (SRA), resulting in gigabytes of aligned homologs for CASP15 targets. These were merged with default MSAs produced by ColabFold-search and provided to ColabFold-predict. By using SRA data, we achieved highly accurate predictions (GDT_TS > 70) for 66% of the non-easy targets, whereas using ColabFold-search default MSAs scored highly in only 52%. Next, we tested the effect of deep homology search and ColabFold's advanced features, such as more recycles, on prediction accuracy. While SRA homologs were most significant for improving ColabFold's CASP15 ranking from 11th to 3rd place, other strategies contributed too. We analyze these in the context of existing strategies to improve prediction.

The current role and evolution of X-ray crystallography in drug discovery and development

INTRODUCTION

Macromolecular X-ray crystallography and cryo-EM are currently the primary techniques used to determine the three-dimensional structures of proteins, nucleic acids, and viruses. Structural information has been critical to drug discovery and structural bioinformatics. The integration of artificial intelligence (AI) into X-ray crystallography has shown great promise in automating and accelerating the analysis of complex structural data, further improving the efficiency and accuracy of structure determination.

AREAS COVERED

This review explores the relationship between X-ray crystallography and other modern structural determination methods. It examines the integration of data acquired from diverse biochemical and biophysical techniques with those derived from structural biology. Additionally, the paper offers insights into the influence of AI on X-ray crystallography, emphasizing how integrating AI with experimental approaches can revolutionize our comprehension of biological processes and interactions.

EXPERT OPINION

Investing in science is crucially emphasized due to its significant role in drug discovery and advancements in healthcare. X-ray crystallography remains an essential source of structural biology data for drug discovery. Recent advances in biochemical, spectroscopic, and bioinformatic methods, along with the integration of AI techniques, hold the potential to revolutionize drug discovery when effectively combined with robust data management practices.

Machine Learning Generation of Dynamic Protein Conformational Ensembles

Machine learning has achieved remarkable success across a broad range of scientific and engineering disciplines, particularly its use for predicting native protein structures from sequence information alone. However, biomolecules are inherently dynamic, and there is a pressing need for accurate predictions of dynamic structural ensembles across multiple functional levels. These problems range from the relatively well-defined task of predicting conformational dynamics around the native state of a protein, which traditional molecular dynamics (MD) simulations are particularly adept at handling, to generating large-scale conformational transitions connecting distinct functional states of structured proteins or numerous marginally stable states within the dynamic ensembles of intrinsically disordered proteins. Machine learning has been increasingly applied to learn low-dimensional representations of protein conformational spaces, which can then be used to drive additional MD sampling or directly generate novel conformations. These methods promise to greatly reduce the computational cost of generating dynamic protein ensembles, compared to traditional MD simulations. In this review, we examine recent progress in machine learning approaches towards generative modeling of dynamic protein ensembles and emphasize the crucial importance of integrating advances in machine learning, structural data, and physical principles to achieve these ambitious goals.