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Identifying the interactome for a protein of interest is challenging due to the large number of possible binders. High‐throughput experimental approaches narrow down possible binding partners but often include false positives. Furthermore, they provide no information about what the binding region is (e.g., the binding epitope). We introduce a novel computational pipeline based on an AlphaFold2 (AF) Competitive Binding Assay (AF‐CBA) to identify proteins that bind a target of interest from a pull‐down experiment and the binding epitope. Our focus is on proteins that bind the Extraterminal (ET) domain of Bromo and Extraterminal domain (BET) proteins, but we also introduce nine additional systems to show transferability to other peptide‐protein systems. We describe a series of limitations to the methodology based on intrinsic deficiencies of AF and AF‐CBA to help users identify scenarios where the approach will be most useful. Given the method's speed and accuracy, we anticipate its broad applicability to identify binding epitope regions among potential partners, setting the stage for experimental verification. [ABSTRACT FROM AUTHOR]

Peptide‐based drugs offer high specificity, potency, and selectivity. However, their inherent flexibility and differences in conformational preferences between their free and bound states create unique challenges that have hindered progress in effective drug discovery pipelines. The emergence of AlphaFold (AF) and Artificial Intelligence (AI) presents new opportunities for enhancing peptide‐based drug discovery. We explore recent advancements that facilitate a successful peptide drug discovery pipeline, considering peptides' attractive therapeutic properties and strategies to enhance their stability and bioavailability. AF enables efficient and accurate prediction of peptide‐protein structures, addressing a critical requirement in computational drug discovery pipelines. In the post‐AF era, we are witnessing rapid progress with the potential to revolutionize peptide‐based drug discovery such as the ability to rank peptide binders or classify them as binders/non‐binders and the ability to design novel peptide sequences. However, AI‐based methods are struggling due to the lack of well‐curated datasets, for example to accommodate modified amino acids or unconventional cyclization. Thus, physics‐based methods, such as docking or molecular dynamics simulations, continue to hold a complementary role in peptide drug discovery pipelines. Moreover, MD‐based tools offer valuable insights into binding mechanisms, as well as the thermodynamic and kinetic properties of complexes. As we navigate this evolving landscape, a synergistic integration of AI and physics‐based methods holds the promise of reshaping the landscape of peptide‐based drug discovery. This article is categorized under:Structure and Mechanism > Computational Biochemistry and BiophysicsMolecular and Statistical Mechanics > Molecular InteractionsSoftware > Molecular Modeling [ABSTRACT FROM AUTHOR]

AlphaFold has revolutionized structural biology by predicting highly accurate structures of proteins and their complexes with peptides and other proteins. However, for protein‐peptide systems, we are also interested in identifying the highest affinity binder among a set of candidate peptides. We present a novel competitive binding assay using AlphaFold to predict structures of the receptor in the presence of two peptides. For systems in which the individual structures of the peptides are well predicted, the assay captures the higher affinity binder in the bound state, and the other peptide in the unbound form with statistical significance. We test the application on six protein receptors for which we have experimental binding affinities to several peptides. We find that the assay is best suited for identifying medium to strong peptide binders that adopt stable secondary structures upon binding. [ABSTRACT FROM AUTHOR]

In this study, the presence of rare missense variants and frame shift deletions in the I TECTA i gene in six unrelated families with MD suggests a role of this gene in the pathophysiology of the disease. Three rare missense variants (colored in blue) and two short deletions (colored in red) were found in four multicase MD families (arrow shaped) and two additional families with one MD patient and relatives with partial syndromes (diamond shaped). Dear Editor Although there have been considerable advances in recent years, the contribution of genetic factors to Meniere's disease (MD) is not yet fully understood. [Extracted from the article]

Background: Previous studies have identified that patients with EGFR mutations tend to have better responses to targeted therapy, as well as chemotherapy; however, the effect of genetic alterations in terms of radiotherapy (RT)‐related outcomes has not been fully assessed. We studied the impact of common non‐small cell lung cancer (NSCLC) genetic alterations (EGFR, ALK and KRAS) in relation to objective response rate (ORR) to RT in patients with brain metastases. Methods: From 2009–2015, 153 patients with an available genotyping status were treated with whole‐brain irradiation (WBI) before receiving systemic therapy. Primary outcome was ORR; secondary outcomes included intracranial progression‐free survival (IPFS) and overall survival (OS). Results: Overall, ORR was 47.1%. ORR to RT varied significantly according to molecular status: EGFR (64.5%) ALK (54.5%) KRAS (20%) and WT (35.4%) (P = 0.001). EGFR mutation was the only independently associated factor for response to WBI (RR 3.52 [95% CI 1.6–7.7]; P = 0.002). Median IPFS was 10.8 months [95% CI 8.2–13.5] overall; however, IPFS also varied significantly according to molecular status: EGFR (18.2 months), ALK (18.4 months), KRAS (6.0 months) and WT (8.7 months) (P < 0.0001). OS for EGFR, ALK, KRAS and WT patients was 36.6, 32.2, 15.5 and 22.4 months, respectively (P = 0.014). Intracranial‐ORR (HR 0.4 [95% CI 0.2–0.6], P < 0.001) and mutation status (HR 0.7 [95% CI 0.6–0.9], P < 0.042) were independently associated with a higher OS. Conclusions: RT response varies as per tumor molecular status. The presence of EGFR mutations favors the organ‐specific response to RT, and is associated with longer OS in patients with NSCLC and BM. Key points: This study addressed for the first time the difference in radiotherapy‐related outcomes in patients with different genotypes of non‐small cell lung cancer (NSCLC) before they received systemic therapy.Results show that response to radiotherapy varies as per tumor molecular status, particularly EGFR‐mutated tumors, have a favorable response to radiotherapy, contrary to KRAS‐mutated tumors. [ABSTRACT FROM AUTHOR]

We describe the performance of MELD‐accelerated molecular dynamics (MELDxMD) in determining protein structures in the NMR‐data‐assisted category in CASP13. Seeded from web server predictions, MELDxMD was found best in the NMR category, over 17 targets, outperforming the next‐best groups by a factor of ~4 in z‐score. MELDxMD gives ensembles, not single structures; succeeds on a 326‐mer, near the current upper limit for NMR structures; and predicts structures that match experimental residual dipolar couplings even though the only NMR‐derived data used in the simulations was NOE‐based ambiguous atom–atom contacts and backbone dihedrals. MELD can use noisy and ambiguous experimental information to reduce the MD search space. We believe MELDxMD is a promising method for determining protein structures from NMR data. [ABSTRACT FROM AUTHOR]

There is a pressing need for new computational tools to integrate data from diverse experimental approaches in structural biology. We present a strategy that combines sparse paramagnetic solid‐state NMR restraints with physics‐based atomistic simulations. Our approach explicitly accounts for uncertainty in the interpretation of experimental data through the use of a semi‐quantitative mapping between the data and the restraint energy that is calibrated by extensive simulations. We apply our approach to solid‐state NMR data for the model protein GB1 labeled with Cu2+‐EDTA at six different sites. We are able to determine the structure to 0.9 Å accuracy within a single day of computation on a GPU cluster. We further show that in some cases, the data from only a single paramagnetic tag are sufficient for accurate folding. [ABSTRACT FROM AUTHOR]

It is challenging to compute structure-function relationships of proteins using molecular physics. The problem arises from the exponential scaling of the computational searching and sampling of large conformational spaces. This scaling challenge is not met by today's methods, such as Monte Carlo, simulated annealing, genetic algorithms, or molecular dynamics (MD) or its variants such as replica exchange. Such methods of searching for optimal states on complex probabilistic landscapes are referred to more broadly as Explore-and-Exploit (EE), including in contexts such as computational learning, games, industrial planning, and modeling military strategies. Here, we describe a Bayesian method, called MELD, that 'melds' together EE approaches with externally added information that can be vague, combinatoric, noisy, intuitive, heuristic, or from experimental data. MELD is shown to accelerate physical MD simulations when using experimental data to determine protein structures; for predicting protein structures by using heuristic directives; and when predicting binding affinities of proteins from limited information about the binding site. Such Guided EE approaches might also be useful beyond proteins and beyond molecular science. WIREs Comput Mol Sci 2017, 7:e1309. doi: 10.1002/wcms.1309 For further resources related to this article, please visit the . [ABSTRACT FROM AUTHOR]

RNA-induced silencing is a process which allows cells to regulate the synthesis of specific proteins. RNA silencing is promoted by the protein C3PO (component 3 of RISC). We have previously found that phospholipase Cβ, which increases intracellular calcium levels in response to specific G protein signals, inhibits C3PO activity towards certain genes. Understanding the parameters that control C3PO activity and which genes are impacted by G protein activation would help predict which genes are more vulnerable to downregulation. Here, using a library of 1018 oligonucleotides, we show that C3PO binds oligonucleotides with structural specificity but little sequence specificity. Alternately, C3PO hydrolyzes oligonucleotides with a rate that is sensitive to substrate stability. Importantly, we find that oligonucleotides with higher Tm values are inhibited by bound PLCβ. This finding is supported by microarray analysis in cells over-expressing PLCβ1. Taken together, this study allows predictions of the genes whose post-transcriptional regulation is responsive to the G protein/phospholipase Cβ/calcium signaling pathway. [ABSTRACT FROM AUTHOR]

ABSTRACT A large number of methods generate conformational ensembles of biomolecules. Often one structure is selected to be representative of the whole ensemble, usually by clustering and selecting the structure closest to the center of the most populated cluster. We find that this structure is not necessarily the best representation of the cluster and present here two computationally inexpensive averaging protocols that can systematically provide better representations of the system, which can be more directly compared with structures from X-ray crystallography. In practice, systematic errors in the generated conformational ensembles appear to limit the maximum improvement of averaging methods. Proteins 2014; 82:2671-2680. © 2014 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

The authors evaluated the impact of foreign aid to five services sectors (transportation, information and communications technologies ( ICT), energy, banking/financial services, and business services) on exports of downstream manufacturing sectors in developing countries. To address the reverse causality between aid and exports, they relied on an original identification strategy that exploits (i) the variation of aid flows to services sectors and (ii) the variation of service intensities across industrial sectors and countries using input-output data. They found a positive effect of aid to services, in general, on downstream manufacturing exports of developing countries across regions and income-level groups. [ABSTRACT FROM AUTHOR]

Product standards can have a dual impact on costs and, thus, on trade. They can impose costs on exporters as it may be necessary to adapt products for specific markets (cost effect). In contrast, standards can reduce exporters' information costs as they convey information on product characteristics (informational effect). Using a new World Bank database of European standards for electronic products, we examine the net impact of internationally harmonised European standards on European Union imports. We find that European Union standards for electronic products that are harmonised to international standards have a significant and a positive net effect on trade. The results suggest that efforts to promote trade in electronic products could be complemented by steps to promote standards harmonisation. This might include, for example, restarting talks to extend the WTO's Information Technology Agreement to commitments to harmonise national standards in electronic products. [ABSTRACT FROM AUTHOR]