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The COVID‐19 pandemic continues to pose a substantial threat to human lives and is likely to do so for years to come. Despite the availability of vaccines, searching for efficient small‐molecule drugs that are widely available, including in low‐ and middle‐income countries, is an ongoing challenge. In this work, we report the results of an open science community effort, the "Billion molecules against COVID‐19 challenge", to identify small‐molecule inhibitors against SARS‐CoV‐2 or relevant human receptors. Participating teams used a wide variety of computational methods to screen a minimum of 1 billion virtual molecules against 6 protein targets. Overall, 31 teams participated, and they suggested a total of 639,024 molecules, which were subsequently ranked to find 'consensus compounds'. The organizing team coordinated with various contract research organizations (CROs) and collaborating institutions to synthesize and test 878 compounds for biological activity against proteases (Nsp5, Nsp3, TMPRSS2), nucleocapsid N, RdRP (only the Nsp12 domain), and (alpha) spike protein S. Overall, 27 compounds with weak inhibition/binding were experimentally identified by binding‐, cleavage‐, and/or viral suppression assays and are presented here. Open science approaches such as the one presented here contribute to the knowledge base of future drug discovery efforts in finding better SARS‐CoV‐2 treatments. [ABSTRACT FROM AUTHOR]

Background: Pyruvate dehydrogenase complex deficiency (PDCD) is a mitochondrial neurometabolic disorder of energy deficit, with incidence of about 1 in 42,000 live births annually in the USA. The median and mean ages of diagnosis of PDCD are about 12 and 31 months, respectively. PDCD is a major cause of primary lactic acidosis with concomitant elevation in blood alanine (Ala) and proline (Pro) concentrations depending on phenotypic severity. Alanine/Leucine (Ala/Leu) ≥4.0 and Proline/Leucine (Pro/Leu) ≥3.0 combination cutoff from dried blood spot specimens was used as a biomarker for early identification of neonates/infants with PDCD. Further investigations were needed to evaluate the sensitivity (SN), specificity (SP), and clinical utility of such amino acid (AA) ratio combination cutoffs in discriminating PDCD from other inborn errors of metabolism (IEM) for early identification of such patients. Methods: We reviewed medical records of patients seen at UPMC in the past 11 years with molecularly or enzymatically confirmed diagnosis. We collected plasma AA analysis data from samples prior to initiation of therapeutic interventions such as total parenteral nutrition and/or ketogenic diet. Conditions evaluated included organic acidemias, primary mitochondrial disorders (MtDs), fatty acid oxidation disorders (FAOD), other IEMs on current newborn screening panels, congenital cardiac great vessel anomalies, renal tubular acidosis, and non‐IEMs. The utility of specific AA ratio combinations as biomarkers were evaluated using receiver operating characteristic curves, correlation analysis, principal component analysis, and cutoff SN, SP, and positive predictive value determined from 201 subjects with broad age range. Results: Alanine/Lysine (Ala/Lys) and Ala/Leu as well as (Ala + Pro)/(Leu + Lys) and Ala/Leu ratio combinations effectively discriminated subjects with PDCD from those with other MtDs and IEMs on current newborn screening panels. Specific AA ratio combinations were significantly more sensitive in identifying PDCD than Ala alone or combinations of Ala and/or Pro in the evaluated cohort of subjects. Ala/Lys ≥3.0 and Ala/Leu ≥5.0 as well as (Ala + Pro)/(Leu + Lys) ≥2.5 and Ala/Leu ≥5.0 combination cutoffs identified patients with PDCD with 100% SN and ~85% SP. Conclusions: With the best predictor of survival and positive cognitive outcome in PDCD being age of diagnosis, PDCD patients would benefit from use of such highly SN and SP AA ratio combination cutoffs as biomarkers for early identification of at‐risk newborns, infants, and children, for early intervention(s) with known and/or novel therapeutics for this disorder. [ABSTRACT FROM AUTHOR]

The potential energy of molecular species and their conformers can be computed with a wide range of computational chemistry methods, from molecular mechanics to ab initio quantum chemistry. However, the proper choice of the computational approach based on computational cost and reliability of calculated energies is a dilemma, especially for large molecules. This dilemma is proved to be even more problematic for studies that require hundreds and thousands of calculations, such as drug discovery. On the other hand, driven by their pattern recognition capabilities, neural networks started to gain popularity in the computational chemistry community. During the last decade, many neural network potentials have been developed to predict a variety of chemical information of different systems. Neural network potentials are proved to predict chemical properties with accuracy comparable to quantum mechanical approaches but with the cost approaching molecular mechanics calculations. As a result, the development of more reliable, transferable, and extensible neural network potentials became an attractive field of study for researchers. In this review, we outlined an overview of the status of current neural network potentials and strategies to improve their accuracy. We provide recent examples of studies that prove the applicability of these potentials. We also discuss the capabilities and shortcomings of the current models and the challenges and future aspects of their development and applications. It is expected that this review would provide guidance for the development of neural network potentials and the exploitation of their applicability. This article is categorized under:Data Science > Artificial Intelligence/Machine LearningMolecular and Statistical Mechanics > Molecular InteractionsSoftware > Molecular Modeling [ABSTRACT FROM AUTHOR]

Cryo-EM and X-ray crystallography provide crucial experimental data for obtaining atomic-detail models of biomacromolecules. Refining these models relies on library-based stereochemical restraints, which, in addition to being limited to known chemical entities, do not include meaningful noncovalent interactions relying solely on nonbonded repulsions. Quantum mechanical (QM) calculations could alleviate these issues but are too expensive for large molecules. We present a novel AI-enabled Quantum Refinement (AQuaRef) based on AIMNet2 neural network potential mimicking QM at substantially lower computational costs. By refining 41 cryo-EM and 30 X-ray structures, we show that this approach yields atomic models with superior geometric quality compared to standard techniques, while maintaining an equal or better fit to experimental data., Competing Interests: Competing Interests The authors declare no competing interests.

The behavior of proteins is closely related to the protonation states of the residues. Therefore, prediction and measurement of p K a are essential to understand the basic functions of proteins. In this work, we develop a new empirical scheme for protein p K a prediction that is based on deep representation learning. It combines machine learning with atomic environment vector (AEV) and learned quantum mechanical representation from ANI-2x neural network potential (J. Chem. Theory Comput. 2020, 16, 4192). The scheme requires only the coordinate information of a protein as the input and separately estimates the p K a for all five titratable amino acid types. The accuracy of the approach was analyzed with both cross-validation and an external test set of proteins. Obtained results were compared with the widely used empirical approach PROPKA. The new empirical model provides accuracy with MAEs below 0.5 for all amino acid types. It surpasses the accuracy of PROPKA and performs significantly better than the null model. Our model is also sensitive to the local conformational changes and molecular interactions., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Semi-empirical quantum methods from the neglect of differential diatomic overlap (NDDO) family such as MNDO, AM1, or PM3 are fast albeit approximate quantum methods. By combining them with linear scaling methods like the divide & conquer (D&C) method, it is possible to quickly evaluate the energy of systems containing hundreds to thousands of atoms. We here present our implementation in the Amber biomolecular package of a SEBOMD module that provides a way to run semi-empirical Born-Oppenheimer molecular dynamics. At each step of a SEBOMD, a fully converged self-consistent field (SCF) calculation is performed to obtain the semiempirical quantum potential energy of a molecular system encaged or not in periodic boundary conditions. We describe the implementation and the features of our SEBOMD implementation. We show the requirements to conserve the total energy in NVE simulations, and how to accelerate SCF convergence through density matrix extrapolation. Specific ways of handling periodic boundary conditions using mechanical embedding or electrostatic embedding through a tailored quantum Ewald summation is developed. The parallel performance of SEBOMD simulations using the D&C scheme are presented for liquid water systems of various sizes, and a comparison between the traditional full diagonalization scheme and the D&C approach for the reproduction of the structure of liquid water illustrates the potentiality of SEBOMD to simulate molecular systems containing several hundreds of atoms for hundreds of picoseconds with a quantum mechanical potential in a reasonable amount of CPU time.