Overview of AlphaFold2 and breakthroughs in overcoming its limitations

Computers in Biology and Medicine, Год журнала: 2024, Номер 176, С. 108620 - 108620

Опубликована: Май 15, 2024

Язык: Английский

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Old Moats for New Models: Openness, Control, and Competition in Generative Ai

SSRN Electronic Journal, Год журнала: 2024, Номер unknown

Опубликована: Янв. 1, 2024

Drawing insights from the field of innovation economics, we discuss likely competitive environment shaping generative AI advances. Central to our analysis are concepts appropriability—whether firms in industry able control knowledge generated by their innovations—and complementary assets—whether effective entry requires access specialized infrastructure and capabilities which incumbent can ration access. While rapid improvements foundation models promise transformative impacts across broad sectors economy, argue that tight over assets will result a concentrated market structure, as past episodes technological upheaval. We suggest paths through may restrict entry, confining newcomers subordinate roles stifling sectoral innovation. conclude with speculations regarding how this oligopolistic future might be averted. Policy interventions aimed at fractionalizing or facilitating shared help preserve competition incentives for extending frontier. Ironically, best hopes vibrant open source ecosystem rest on presence "rogue" technology giant, who choose openness engagement smaller strategic weapon wielded against other incumbents.Institutional subscribers NBER working paper series, residents developing countries download without additional charge www.nber.org.

Язык: Английский

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

Frontiers in Immunology, Год журнала: 2024, Номер 15

Опубликована: Апрель 16, 2024

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

Язык: Английский

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Leveraging protein structural information to improve variant effect prediction

Current Opinion in Structural Biology, Год журнала: 2025, Номер 92, С. 103023 - 103023

Опубликована: Фев. 22, 2025

Despite massive sequencing efforts, understanding the difference between human pathogenic and benign variants remains a challenge. Computational variant effect predictors (VEPs) have emerged as essential tools for assessing impact of genetic variants, although their performance varies. Initially, sequence-based methods dominated field, but recent advances, particularly in protein structure prediction technologies like AlphaFold, led to an increased utilization structural information by VEPs aimed at scoring missense variants. This review highlights progress integrating into VEPs, showcasing novel models such AlphaMissense, PrimateAI-3D, CPT-1 that demonstrate improved evaluation. Structural data offers more interpretability, especially non-loss-of-function provides insights complex interactions vivo. As field utilizing biomolecular structures will be pivotal future VEP development, with breakthroughs protein-ligand protein-nucleic acid offering new avenues.

Язык: Английский

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The Physics-AI Dialogue in Drug Design

RSC Medicinal Chemistry, Год журнала: 2025, Номер unknown

Опубликована: Янв. 1, 2025

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

Язык: Английский

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Molecular Engineering of Alginate Lyases and the Potential Agricultural Applications of Their Enzymatic Products

Journal of Agricultural and Food Chemistry, Год журнала: 2025, Номер unknown

Опубликована: Фев. 26, 2025

Alginate lyases, enzymes that degrade alginate into unsaturated oligosaccharides, have attracted significant attention for their potential applications across various fields, particularly in agriculture. This review focuses on the molecular engineering of lyases to enhance activity, stability, and specificity as well agricultural resulting enzymatic products, known oligosaccharides (AOS). We start by summarizing sources classification followed recent advances through directed evolution, rational design, truncation noncatalytic domains, conserved domain reconstruction. then explore diverse AOS, including ability promote plant growth, increase content active components, extend fruit shelf life, resistance abiotic stresses. Furthermore, value AOS feed additives preservatives shrimp-based products is also assessed. will not only lay a solid theoretical foundation but serve catalyst innovative development practical application high-value preparations utilization AOS-related providing new solutions sustainable agriculture food industry.

Язык: Английский

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Peptides of a Feather: How Computation Is Taking Peptide Therapeutics under Its Wing

Genes, Год журнала: 2023, Номер 14(6), С. 1194 - 1194

Опубликована: Май 29, 2023

Leveraging computation in the development of peptide therapeutics has garnered increasing recognition as a valuable tool to generate novel for disease-related targets. To this end, transformed field design through identifying that exhibit enhanced pharmacokinetic properties and reduced toxicity. The process

Язык: Английский

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Extremozymes: Challenges and opportunities on the road to novel enzymes production

Process Biochemistry, Год журнала: 2024, Номер 143, С. 323 - 336

Опубликована: Май 3, 2024

Язык: Английский

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Directed Evolution of Protoglobin Optimizes the Enzyme Electric Field

Journal of the American Chemical Society, Год журнала: 2024, Номер 146(24), С. 16670 - 16680

Опубликована: Июнь 7, 2024

To unravel why computational design fails in creating viable enzymes, while directed evolution (DE) succeeds, our research delves into the laboratory of protoglobin. DE has adapted this protein to efficiently catalyze carbene transfer reactions. We show that previously proposed enhanced substrate access and binding alone cannot account for increased yields during DE. The 3D electric field entire active site is tracked through dynamics, clustered using affinity propagation algorithm, subjected principal component analysis. This analysis reveals notable changes with DE, where distinct topologies influence transition state energetics mechanism. A chemically meaningful emerges takes lead facilitates crossing barrier transfer. Our findings underscore intrinsic dynamic's on enzyme function, ability switch mechanisms within same protein, crucial role design.

Язык: Английский

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

International Journal of Molecular Sciences, Год журнала: 2024, Номер 25(18), С. 10139 - 10139

Опубликована: Сен. 21, 2024

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

Язык: Английский

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