NMRExtractor is a large language model-powered pipeline that automatically extracts experimental NMR data from massive open-access publications, resulting in the construction of NMRBank—the largest open-access NMR dataset available to date. <<<

Anna Lauko, Samuel J. Pellock, Kiera H. Sumida, Ivan Anishchenko, David Juergens, Woody Ahern, Jihun Jeung, Alexander F. Shida, Andrew Hunt, Indrek Kalvet, Christoffer Norn, Ian R. Humphreys, Cooper Jamieson, Rohith Krishna, Yakov Kipnis, Alex Kang, Evans Brackenbrough, Asim K. Bera, Banumathi Sankaran, K. N. Houk, David Baker <<<

The design of enzymes with complex active sites that mediate multistep reactions remains an outstanding challenge. With serine hydrolases as a model system, we combined the generative capabilities of RFdiffusion with an ensemble generation method for assessing active site preorganization at each step in the reaction to design enzymes starting from minimal active site descriptions. Experimental characterization revealed catalytic efficiencies ( k cat / K m ) up to 2.2 × 10 5 M −1 s −1 and crystal structures that closely match the design models (Cα root mean square deviations <1 angstrom). Selection for structural compatibility across the reaction coordinate enabled identification of new catalysts remove with five different folds distinct from those of natural serine hydrolases. Our de novo approach provides insight into the geometric basis of catalysis and a roadmap for designing enzymes that catalyze multistep transformations. <<<

Abstract Accurate prediction of enzyme kinetic parameters is crucial for enzyme exploration and modification. Existing models face the problem of either low accuracy or poor generalization ability due to overfitting. In this work, we first developed unbiased datasets to evaluate the actual performance of these methods and proposed a deep learning model, CataPro, based on pre-trained models and molecular fingerprints to predict turnover number (k c a t ), Michaelis constant (K m ), and catalytic efficiency (k c a t /K m ). Compared with previous baseline models, CataPro demonstrates clearly enhanced accuracy and generalization ability on the unbiased datasets. In a representational enzyme mining project, by combining CataPro with traditional methods, we identified an enzyme (SsCSO) with 19.53 times increased activity compared to the initial enzyme (CSO2) and then successfully engineered it to improve its activity by 3.34 times. This reveals the high potential of CataPro as an effective tool for future enzyme discovery and modification. <<<

Abstract NPBS (Natural Products & Biological Sources) database is a chemical data resource with relational data between natural products and biological sources, manually curated from literatures of natural product researches. The relational data link a specific species and all the natural products derived from it and contrarily link a specific natural product and all the biological sources. The biological sources cover diverse species of plant, bacterial, fungal and marine organisms; the natural molecules have proper chemical structure data and computable molecular properties and all the relational data have corresponding references. NPBS database provides a wider choice of biological sources and can be used for dereplication to prevent re-isolation and re-characterization of already known natural products. Database URL: http://www.organchem.csdb.cn/scdb/NPBS <<<

Ivan Anishchenko, Yakov Kipnis, Indrek Kalvet, Guangfeng Zhou, Rohith Krishna, Samuel J. Pellock, Anna Lauko, Gyu Rie Lee, Linna An, Justas Dauparas, Frank DiMaio, David Baker <<<

AbstractModeling the conformational heterogeneity of protein-small molecule systems is an outstanding challenge. We reasoned that while residue level descriptions of biomolecules are efficient for de novo structure prediction, for probing heterogeneity of interactions with small molecules in the folded state an entirely atomic level description could have advantages in speed and generality. We developed a graph neural network called ChemNet trained to recapitulate correct atomic positions from partially corrupted input structures from the Cambridge Structural Database and the Protein Data Bank; the nodes of the graph are the atoms in the system. ChemNet accurately generates structures of diverse organic small molecules given knowledge of their atom composition and bonding, and given a description of the larger protein context, and builds up structures of small molecules and protein side chains for protein-small molecule docking. Because ChemNet is rapid and stochastic, ensembles of predictions can be readily generated to map conformational heterogeneity. In enzyme design efforts described here and elsewhere, we find that using ChemNet to assess the accuracy and pre-organization of the designed active sites results in higher success rates and higher activities; we obtain a preorganized retroaldolase with akcat/KMof 11000 M-1min- 1, considerably higher than any pre-deep learning design for this reaction. We anticipate that ChemNet will be widely useful for rapidly generating conformational ensembles of small molecule and small molecule-protein systems, and for designing higher activity preorganized enzymes. <<<

The grand challenge in structure-based drug design is achieving accurate prediction of binding free energies. Molecular dynamics (MD) simulations enable modeling of conformational changes critical to the binding process, leading to calculation of thermodynamic quantities involved in estimation of binding affinities. With recent advancements in computing capability and predictive accuracy, MD based virtual screening has progressed from the domain of theoretical attempts to real application in drug development. Approaches including the Molecular Mechanics Poisson Boltzmann Surface Area (MM-PBSA), Linear Interaction Energy (LIE), and alchemical methods have been broadly applied to model molecular recognition for drug discovery and lead optimization. Here we review the varied methodology of these approaches, developments enhancing simulation efficiency and reliability, remaining challenges hindering predictive performance, and applications to problems in the fields of medicine and biochemistry. <<<

AbstractThe rational targeting of RNA with small molecules is hampered by our still limited understanding of RNA structural and dynamic properties. Mostin silicotools for binding site identification rely on static structures and therefore cannot face the challenges posed by the dynamic nature of RNA molecules. Here, we present SHAMAN, a computational technique to identify potential small-molecule binding sites in RNA structural ensembles. SHAMAN enables exploring the conformational landscape of RNA with atomistic molecular dynamics and at the same time identifying RNA pockets in an efficient way with the aid of probes and enhanced-sampling techniques. In our benchmark composed of large, structured riboswitches as well as small, flexible viral RNAs, SHAMAN successfully identified all the experimentally resolved pockets and ranked them among the most favorite probe hotspots. Overall, SHAMAN sets a solid foundation for future drug design efforts targeting RNA with small molecules, effectively addressing the long-standing challenges in the field. <<<

INTRODUCTION: KRAS is a critical oncogenic protein intricately involved in tumor progression, and the difficulty in targeting KRAS has led it to be classified as an 'undruggable target.' Among the various KRAS mutations, KRASG12D is highly prevalent and represents a promising therapeutic target, yet there are currently no approved inhibitors for it.AREA COVERED: This review summarizes numerous patents and literature featuring inhibitors or degraders of KRASG12D through searching relevant information in PubMed, SciFinder and Web of Science databases from 2021 to February 2024, providing an overview of the research progress on inhibiting KRASG12D in terms of design strategies, chemical structures, biological activities, and clinical advancements.EXPERT OPINION: Since the approval of AMG510 (Sotorasib), there has been an increasing focus on the inhibition of KRASG12D, leading to numerous reports of related inhibitors and degraders. Among them, MRTX1133, as the first KRASG12D inhibitor to enter clinical trials, has demonstrated excellent tumor suppression in various KRASG12D-bearing human tumor xenograft models. It is important to note, however, that understanding the mechanisms of acquired resistance caused by KRAS inhibition and developing additional combination therapies is crucial. Moreover, seeking covalent inhibition of KRASG12D also holds significant potential. <<<

Growing evidence indicates that collagen-binding integrins are important costimulatory molecules of effector T cells. In this study, we demonstrate that the major collagen-binding integrin expressed by human Th17 cells is alpha2beta1 (α2β1) or VLA-2, also known as the receptor for collagen I on T cells. Our results show that human naïve CD4(+) T cells cultured under Th17 polarization conditions preferentially upregulate α2β1 integrin rather than α1β1 integrin, which is the receptor for collagen IV on T cells. Double staining analysis for integrin receptors and intracellular IL-17 showed that α2 integrin but not α1 integrin is associated with Th17 cells. Cell adhesion experiments demonstrated that Th17 cells attach to collagen I and collagen II using α2β1 integrin but did not attach to collagen IV. Functional studies revealed that collagens I and II but not collagen IV costimulate the production of IL-17A, IL-17F and IFN-γ by human Th17 cells activated with anti-CD3. These results identify α2β1 integrin as the major collagen receptor expressed on human Th17 cells and suggest that it can be an important costimulatory molecule of Th17 cell responses. <<<

Abstract Collagen structure in biological tissues imparts its intrinsic physical properties by the formation of several covalent crosslinks. For the first time, two major crosslinks in the skin dihydroxylysinonorleucine (HLNL) and histidinohydroxymerodesmosine (HHMD), were isotopically labelled and then analysed by liquid-chromatography high-resolution accurate-mass mass spectrometry (LC-HRMS) and small-angle neutron scattering (SANS). The isotopic labelling followed by LC-HRMS confirmed the presence of one imino group in both HLNL and HHMD, making them more susceptible to degrade at low pH. The structural changes in collagen due to extreme changes in the pH and chrome tanning were highlighted by the SANS contrast variation between isotopic labelled and unlabelled crosslinks. This provided a better understanding of the interaction of natural crosslinks with the chromium sulphate in collagen suggesting that the development of a benign crosslinking method can help retain the intrinsic physical properties of the leather. This analytical method can also be applied to study artificial crosslinking in other collagenous tissues for biomedical applications. Graphical abstract <<<

Confocal Raman spectroscopy (CRS) is a powerful tool that has been widely used for biological tissue analysis because of its noninvasive nature, high specificity, and rich biochemical information. However, current commercial CRS systems suffer from limited detection regions (450-1750 cm), bulky sizes, nonflexibilities, slow acquisitions by consecutive excitations, and high costs if using a Fourier transform (FT) Raman spectroscopy with an InGaAs detector, which impede their adoption in clinics. In this study, we developed a portable CRS system with a simultaneous dual-wavelength source and a miniaturized handheld probe (120 mm × 60 mm × 50 mm) that can acquire spectra in both fingerprint (FP, 450-1750 cm) and high wavenumber (HW, 2800-3800 cm) regions simultaneously. An innovative design combining 671 and 785 nm lasers for simultaneous excitation through a compact and high-efficiency (>90%) wavelength combiner was implemented. Moreover, to decouple the fused FP and HW spectra, a first-of-its-kind precise Raman spectra separation algorithm (PRSSA) was developed based on the maximum probability (MAP) estimate. The accuracy of spectra separation was greater than 99%, demonstrated in both phantom experiments and human skin measurements. The total data acquisition time was reduced by greater than 50% compared to other CRS systems. The results proved our proposed CRS system and PRSSA's superior capability in fast and ultrawideband spectra acquisition will significantly improve the integration of CRS in the clinical workflow. <<<

Ruichen Li, Haotian Ye, Du Jiang, Xuelan Wen, Chuwei Wang, Zhe Li, Xiang Li, Di He, Ji Chen, Weiluo Ren, Liwei Wang <<<

Abstract Collagen structure in biological tissues imparts its intrinsic physical properties by the formation of several covalent crosslinks. For the first time, two major crosslinks in the skin dihydroxylysinonorleucine (HLNL) and histidinohydroxymerodesmosine (HHMD), were isotopically labelled and then analysed by liquid-chromatography high-resolution accurate-mass mass spectrometry (LC-HRMS) and small-angle neutron scattering (SANS). The isotopic labelling followed by LC-HRMS confirmed the presence of one imino group in both HLNL and HHMD, making them more susceptible to degrade at low pH. The structural changes in collagen due to extreme changes in the pH and chrome tanning were highlighted by the SANS contrast variation between isotopic labelled and unlabelled crosslinks. This provided a better understanding of the interaction of natural crosslinks with the chromium sulphate in collagen suggesting that the development of a benign crosslinking method can help retain the intrinsic physical properties of the leather. This analytical method can also be applied to study artificial crosslinking in other collagenous tissues for biomedical applications. Graphical abstract <<<

Mammalian collagen has been widely used as a biomedical material. Nevertheless, there are still concerns about the variability between preparations, particularly with the possibility that the products may transmit animal-based diseases. Many groups have examined the possible application of bioengineered mammalian collagens. However, translating laboratory studies into large-scale manufacturing has often proved difficult, although certain yeast and plant systems seem effective. Production of full-length mammalian collagens, with the required secondary modification to give proline hydroxylation, has proved difficult in E. coli. However, recently, a new group of collagens, which have the characteristic triple helical structure of collagen, has been identified in bacteria. These proteins are stable without the need for hydroxyproline and are able to be produced and purified from E. coli in high yield. Initial studies indicate that they would be suitable for biomedical applications. <<<

The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article. <<<

Wenyi Yang, Pingping Wang, Meng Luo, Yideng Cai, Chang Xu, Guangfu Xue, Xiyun Jin, Rui Cheng, Jinhao Que, Fenglan Pang, Yuexin Yang, Huan Nie, Qinghua Jiang, Zhigang Liu, Zhaochun Xu <<<

MOTIVATION: Cell-cell interactions (CCIs) play critical roles in many biological processes such as cellular differentiation, tissue homeostasis, and immune response. With the rapid development of high throughput single-cell RNA sequencing (scRNA-seq) technologies, it is of high importance to identify CCIs from the ever-increasing scRNA-seq data. However, limited by the algorithmic constraints, current computational methods based on statistical strategies ignore some key latent information contained in scRNA-seq data with high sparsity and heterogeneity.RESULTS: Here, we developed a deep learning framework named DeepCCI to identify meaningful CCIs from scRNA-seq data. Applications of DeepCCI to a wide range of publicly available datasets from diverse technologies and platforms demonstrate its ability to predict significant CCIs accurately and effectively. Powered by the flexible and easy-to-use software, DeepCCI can provide the one-stop solution to discover meaningful intercellular interactions and build CCI networks from scRNA-seq data.AVAILABILITY AND IMPLEMENTATION: The source code of DeepCCI is available online at https://github.com/JiangBioLab/DeepCCI. <<<