Biomolecular NMR spectroscopy in the era of artificial intelligence

Biomolecular nuclear magnetic resonance (NMR) spectroscopy and artificial intelligence (AI) have a burgeoning synergy. Deep learning-based structural predictors have forever changed structural biology, yet these tools currently face limitations in accurately characterizing protein dynamics, allostery, and conformational heterogeneity. We begin by highlighting the unique abilities of biomolecular NMR spectroscopy to complement AI-based structural predictions toward addressing these knowledge gaps. We then highlight the direct integration of deep learning approaches into biomolecular NMR methods. AI-based tools can dramatically improve the acquisition and analysis of NMR spectra, enhancing the accuracy and reliability of NMR measurements, thus streamlining experimental processes. Additionally, deep learning enables the development of novel types of NMR experiments that were previously unattainable, expanding the scope and potential of biomolecular NMR spectroscopy. Ultimately, a combination of AI and NMR promises to further revolutionize structural biology on several levels, advance our understanding of complex biomolecular systems, and accelerate drug discovery efforts

Small-molecule sequestration of amyloid-β as a drug discovery strategy for Alzheimer's disease.

Disordered proteins are challenging therapeutic targets, and no drug is currently in clinical use that modifies the properties of their monomeric states. Here, we identify a small molecule (10074-G5) capable of binding and sequestering the intrinsically disordered amyloid-β (Aβ) peptide in its monomeric, soluble state. Our analysis reveals that this compound interacts with Aβ and inhibits both the primary and secondary nucleation pathways in its aggregation process. We characterize this interaction using biophysical experiments and integrative structural ensemble determination methods. We observe that this molecule increases the conformational entropy of monomeric Aβ while decreasing its hydrophobic surface area. We also show that it rescues a Caenorhabditis elegans model of Aβ-associated toxicity, consistent with the mechanism of action identified from the in silico and in vitro studies. These results illustrate the strategy of stabilizing the monomeric states of disordered proteins with small molecules to alter their behavior for therapeutic purposes

Rational design of a conformation-specific antibody for the quantification of Aβ oligomers.

Protein misfolding and aggregation is the hallmark of numerous human disorders, including Alzheimer's disease. This process involves the formation of transient and heterogeneous soluble oligomers, some of which are highly cytotoxic. A major challenge for the development of effective diagnostic and therapeutic tools is thus the detection and quantification of these elusive oligomers. Here, to address this problem, we develop a two-step rational design method for the discovery of oligomer-specific antibodies. The first step consists of an "antigen scanning" phase in which an initial panel of antibodies is designed to bind different epitopes covering the entire sequence of a target protein. This procedure enables the determination through in vitro assays of the regions exposed in the oligomers but not in the fibrillar deposits. The second step involves an "epitope mining" phase, in which a second panel of antibodies is designed to specifically target the regions identified during the scanning step. We illustrate this method in the case of the amyloid β (Aβ) peptide, whose oligomers are associated with Alzheimer's disease. Our results show that this approach enables the accurate detection and quantification of Aβ oligomers in vitro, and in Caenorhabditis elegans and mouse hippocampal tissues

Multistep Inhibition of α-Synuclein Aggregation and Toxicity in Vitro and in Vivo by Trodusquemine.

The aggregation of α-synuclein, an intrinsically disordered protein that is highly abundant in neurons, is closely associated with the onset and progression of Parkinson's disease. We have shown previously that the aminosterol squalamine can inhibit the lipid induced initiation process in the aggregation of α-synuclein, and we report here that the related compound trodusquemine is capable of inhibiting not only this process but also the fibril-dependent secondary pathways in the aggregation reaction. We further demonstrate that trodusquemine can effectively suppress the toxicity of α-synuclein oligomers in neuronal cells, and that its administration, even after the initial growth phase, leads to a dramatic reduction in the number of α-synuclein inclusions in a Caenorhabditis elegans model of Parkinson's disease, eliminates the related muscle paralysis, and increases lifespan. On the basis of these findings, we show that trodusquemine is able to inhibit multiple events in the aggregation process of α-synuclein and hence to provide important information about the link between such events and neurodegeneration, as it is initiated and progresses. Particularly in the light of the previously reported ability of trodusquemine to cross the blood-brain barrier and to promote tissue regeneration, the present results suggest that this compound has the potential to be an important therapeutic candidate for Parkinson's disease and related disorders

A rationally designed bicyclic peptide remodels Aβ42 aggregation in vitro and reduces its toxicity in a worm model of Alzheimer’s disease

Funder: Centre for Misfolding DiseasesAbstract: Bicyclic peptides have great therapeutic potential since they can bridge the gap between small molecules and antibodies by combining a low molecular weight of about 2 kDa with an antibody-like binding specificity. Here we apply a recently developed in silico rational design strategy to produce a bicyclic peptide to target the C-terminal region (residues 31–42) of the 42-residue form of the amyloid β peptide (Aβ42), a protein fragment whose aggregation into amyloid plaques is linked with Alzheimer’s disease. We show that this bicyclic peptide is able to remodel the aggregation process of Aβ42 in vitro and to reduce its associated toxicity in vivo in a C. elegans worm model expressing Aβ42. These results provide an initial example of a computational approach to design bicyclic peptides to target specific epitopes on disordered proteins

Methods of probing the interactions between small molecules and disordered proteins

It is generally recognized that a large fraction of the human proteome is made up of proteins that remain disordered in their native states. Despite the fact that such proteins play key biological roles and are involved in many major human diseases, they still represent challenging targets for drug discovery. A major bottleneck for the identification of compounds capable of interacting with these proteins and modulating their disease-promoting behaviour is the development of effective techniques to probe such interactions. The difficulties in carrying out binding measurements have resulted in a poor understanding of the mechanisms underlying these interactions. In order to facilitate further methodological advances, here we review the most commonly used techniques to probe three types of interactions involving small molecules: (1) those that disrupt functional interactions between disordered proteins; (2) those that inhibit the aberrant aggregation of disordered proteins, and (3) those that lead to binding disordered proteins in their monomeric states. In discussing these techniques, we also point out directions for future developments.Gabriella T. Heller is supported by the Gates Cambridge Trust Scholarship. Francesco A. Aprile is supported by a Senior Research Fellowship award from the Alzheimer’s Society, UK (grant number 317, AS-SF-16-003)

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Combined searches for the production of supersymmetric top quark partners in proton-proton collisions at root s=13 TeV

A combination of searches for top squark pair production using proton-proton collision data at a center-of-mass energy of 13 TeV at the CERN LHC, corresponding to an integrated luminosity of 137 fb(-1) collected by the CMS experiment, is presented. Signatures with at least 2 jets and large missing transverse momentum are categorized into events with 0, 1, or 2 leptons. New results for regions of parameter space where the kinematical properties of top squark pair production and top quark pair production are very similar are presented. Depending on themodel, the combined result excludes a top squarkmass up to 1325 GeV for amassless neutralino, and a neutralinomass up to 700 GeV for a top squarkmass of 1150 GeV. Top squarks with masses from 145 to 295 GeV, for neutralino masses from 0 to 100 GeV, with a mass difference between the top squark and the neutralino in a window of 30 GeV around the mass of the top quark, are excluded for the first time with CMS data. The results of theses searches are also interpreted in an alternative signal model of dark matter production via a spin-0 mediator in association with a top quark pair. Upper limits are set on the cross section for mediator particle masses of up to 420 GeV