Amino acid ratio combinations as biomarkers for discriminating patients with pyruvate dehydrogenase complex deficiency from other inborn errors of metabolism.

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.

A community effort in SARS-CoV-2 drug discovery.

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.

Scalable hybrid deep neural networks/polarizable potentials biomolecular simulations including long-range effects.

Deep-HP is a scalable extension of the Tinker-HP multi-GPU molecular dynamics (MD) package enabling the use of Pytorch/TensorFlow Deep Neural Network (DNN) models. Deep-HP increases DNNs' MD capabilities by orders of magnitude offering access to ns simulations for 100k-atom biosystems while offering the possibility of coupling DNNs to any classical (FFs) and many-body polarizable (PFFs) force fields. It allows therefore the introduction of the ANI-2X/AMOEBA hybrid polarizable potential designed for ligand binding studies where solvent-solvent and solvent-solute interactions are computed with the AMOEBA PFF while solute-solute ones are computed by the ANI-2X DNN. ANI-2X/AMOEBA explicitly includes AMOEBA's physical long-range interactions via an efficient Particle Mesh Ewald implementation while preserving ANI-2X's solute short-range quantum mechanical accuracy. The DNN/PFF partition can be user-defined allowing for hybrid simulations to include key ingredients of biosimulation such as polarizable solvents, polarizable counter ions, etc.… ANI-2X/AMOEBA is accelerated using a multiple-timestep strategy focusing on the model's contributions to low-frequency modes of nuclear forces. It primarily evaluates AMOEBA forces while including ANI-2X ones only via correction-steps resulting in an order of magnitude acceleration over standard Velocity Verlet integration. Simulating more than 10 µs, we compute charged/uncharged ligand solvation free energies in 4 solvents, and absolute binding free energies of host-guest complexes from SAMPL challenges. ANI-2X/AMOEBA average errors are discussed in terms of statistical uncertainty and appear in the range of chemical accuracy compared to experiment. The availability of the Deep-HP computational platform opens the path towards large-scale hybrid DNN simulations, at force-field cost, in biophysics and drug discovery.

Simulations of Pathogenic E1α Variants: Allostery and Impact on Pyruvate Dehydrogenase Complex-E1 Structure and Function.

Pyruvate dehydrogenase complex (PDC) deficiency is a major cause of primary lactic acidemia resulting in high morbidity and mortality, with limited therapeutic options. The E1 component of the mitochondrial multienzyme PDC (PDC-E1) is a symmetric dimer of heterodimers (αß/α'ß') encoded by the PDHA1 and PDHB genes, with two symmetric active sites each consisting of highly conserved phosphorylation loops A and B. PDHA1 mutations are responsible for 82-88% of cases. Greater than 85% of E1α residues with disease-causing missense mutations (DMMs) are solvent-inaccessible, with ∼30% among those involved in subunit-subunit interface contact (SSIC). We performed molecular dynamics simulations of wild-type (WT) PDC-E1 and E1 variants with E1α DMMs at R349 and W185 (residues involved in SSIC), to investigate their impact on human PDC-E1 structure. We evaluated the change in E1 structure and dynamics and examined their implications on E1 function with the specific DMMs. We found that the dynamics of phosphorylation Loop A, which is crucial for E1 biological activity, changes with DMMs that are at least about 15 Å away. Because communication is essential for PDC-E1 activity (with alternating active sites), we also investigated the possible communication network within WT PDC-E1 via centrality analysis. We observed that DMMs altered/disrupted the communication network of PDC-E1. Collectively, these results indicate allosteric effect in PDC-E1, with implications for the development of novel small-molecule therapeutics for specific recurrent E1α DMMs such as replacements of R349 responsible for ∼10% of PDC deficiency due to E1α DMMs.

Prediction of protein pK a with representation learning.

The behavior of proteins is closely related to the protonation states of the residues. Therefore, prediction and measurement of pK a are essential to understand the basic functions of proteins. In this work, we develop a new empirical scheme for protein pK 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 pK 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.

Dynamics of the E. coli ß-Clamp Dimer Interface and Its Influence on DNA Loading.

The ring-shaped sliding clamp proteins have crucial roles in the regulation of DNA replication, recombination, and repair in all organisms. We previously showed that the Escherichia coli ß-clamp is dynamic in solution, transiently visiting conformational states in which Domain 1 at the dimer interface is more flexible and prone to unfolding. This work aims to understand how the stability of the dimer interface influences clamp-opening dynamics and clamp loading by designing and characterizing stabilizing and destabilizing mutations in the clamp. The variants with stabilizing mutations conferred similar or increased thermostability and had similar quaternary structure as compared to the wild type. These variants stimulated the ATPase function of the clamp loader, complemented cell growth of a temperature-sensitive strain, and were successfully loaded onto a DNA substrate. The L82D and L82E I272A variants with purported destabilizing mutations had decreased thermostability, did not complement the growth of a temperature-sensitive strain, and had weakened dimerization as determined by native trapped ion mobility spectrometry-mass spectrometry. The ß L82E variant had a reduced melting temperature but dimerized and complemented growth of a temperature-sensitive strain. All three clamps with destabilizing mutations had perturbed loading on DNA. Molecular dynamics simulations indicate altered hydrogen-bonding patterns at the dimer interface, and cross-correlation analysis showed the largest perturbations in the destabilized variants, consistent with the observed change in the conformations and functions of these clamps.

LICHEM 1.1: Recent Improvements and New Capabilities.

The QM/MM method has become a useful tool to investigate various properties of complex systems. We previously introduced the layered interacting chemical models (LICHEM) package to enable QM/MM simulations with advanced potentials by combining various (unmodified) QM and MM codes ( J. Comp. Chem., 2016, 37, 1019). LICHEM provides several capabilities such as the ability to use polarizable force fields, such as AMOEBA, for the MM environment. Here, we describe an updated version of LICHEM (v1.1), which includes several new functionalities including a new method to account for long-range electrostatic effects in QM/MM (QM/MM-LREC), a new implementation for QM/MM with the Gaussian electrostatic model (GEM), and new capabilities for path optimizations using the quadratic string model (QSM) coupled with restrained MM environment optimization.

Semi-Empirical Born-Oppenheimer Molecular Dynamics (SEBOMD) within the Amber Biomolecular Package.

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.

QM/MM Simulations with the Gaussian Electrostatic Model: A Density-based Polarizable Potential.

The use of advanced polarizable potentials in quantum mechanical/molecular mechanical (QM/MM) simulations has been shown to improve the overall accuracy of the calculation. We have developed a density-based potential called the Gaussian electrostatic model (GEM), which has been shown to provide very accurate environments for QM wave functions in QM/MM. In this contribution we present a new implementation of QM/GEM that extends our implementation to include all components (Coulomb, exchange-repulsion, polarization, and dispersion) for the total intermolecular interaction energy in QM/MM calculations, except for the charge-transfer term. The accuracy of the method is tested using a subset of water dimers from the water dimer potential energy surface reported by Babin et al. ( J. Chem. Theory Comput. 2013 9, 5395-5403). Additionally, results of the new implementation are contrasted with results obtained with the classical AMOEBA potential. Our results indicate that GEM provides an accurate MM environment with average root-mean-square error <0.15 kcal/mol for every intermolecular interaction energy component compared with SAPT2+3/aug-cc-pVTZ reference calculations.

Molecular dynamics simulations of apo, holo, and inactivator bound GABA-at reveal the role of active site residues in PLP dependent enzymes.

The pyridoxal 5-phosphate (PLP) cofactor is a significant organic molecule in medicinal chemistry. It is often found covalently bound to lysine residues in proteins to form PLP dependent enzymes. An example of this family of PLP dependent enzymes is γ-aminobutyric acid aminotransferase (GABA-AT) which is responsible for the degradation of the neurotransmitter GABA. Its inhibition or inactivation can be used to prevent the reduction of GABA concentration in brain which is the source of several neurological disorders. As a test case for PLP dependent enzymes, we have performed molecular dynamics simulations of GABA-AT to reveal the roles of the protein residues and its cofactor. Three different states have been considered: the apoenzyme, the holoenzyme, and the inactive state obtained after the suicide inhibition by vigabatrin. Different protonation states have also been considered for PLP and two key active site residues: Asp298 and His190. Together, 24 independent molecular dynamics trajectories have been simulated for a cumulative total of 2.88 µs. Our results indicate that, unlike in aqueous solution, the PLP pyridine moiety is protonated in GABA-AT. This is a consequence of a pKa shift triggered by a strong charge-charge interaction with an ionic "diad" formed by Asp298 and His190 that would help the activation of the first half-reaction of the catalytic mechanism in GABA-AT: the conversion of PLP to free pyridoxamine phosphate (PMP). In addition, our MD simulations exhibit additional strong hydrogen bond networks between the protein and PLP: the phosphate group is held in place by the donation of at least three hydrogen bonds while the carbonyl oxygen of the pyridine ring interacts with Gln301; Phe181 forms a π-π stacking interaction with the pyridine ring and works as a gate keeper with the assistance of Val300. All these interactions are hypothesized to help maintain free PMP in place inside the protein active site to facilitate the second half-reaction in GABA-AT: the regeneration of PLP-bound GABA-AT (i.e., the holoenzyme). Proteins 2016; 84:875-891. © 2016 Wiley Periodicals, Inc.

Stereoelectronic explanations for the mechanistic details of transimination and HF elimination reactions.

The ß-fluoroamines are commonly used as substrate analogs to determine the mechanistic details of enzymatic reactions. Presence of fluorine atom gives rise to the alterations in the electronic profile and the pKa of molecules which results in mechanistic deviations. The fluorine-substituted mechanism-based substrate analogs are widely used in the inactivation of pyridoxal 5'-phosphate (PLP)-dependent enzymes. The presence of fluorine atom also alters the sequence of reactions taking place in PLP-dependent enzymes where the HF elimination reaction appears in between the transimination and inactivation reactions. Despite the amount of the works on ß-fluoroamines, the effect of stereoelectronic differences on the transimination and HF elimination reactions taking place in PLP-dependent enzymes has not been investigated yet. A density functional theory study is conducted to elucidate mechanistic details of the reactions occurring in PLP-dependent enzymes. In order to understand the mechanistic insights of different isomers and the effect of the fluorine atom, 4-amino-3-fluorobutanoic acid (3-F-GABA) enantiomers are chosen to be investigated besides 4-aminobutanoic acid (GABA), which is the natural substrate for γ-aminobutyric acid aminotransferase (GABA-AT). The investigated ß-fluoroamines are the experimentally proposed potential inhibitors of PLP-dependent enzyme GABA-AT.

Theoretical study on HF elimination and aromatization mechanisms: a case of pyridoxal 5' phosphate-dependent enzyme.

Pyridoxal 5-phosphate (PLP), the phosphorylated and the oxidized form of vitamin B6 is an organic cofactor. PLP forms a Schiff base with the ϵ-amino group of a lysine residue of PLP-dependent enzymes. γ-Aminobutyric acid (GABA) aminotransferase is a PLP-dependent enzyme that degrades GABA to succinic semialdehyde, while reduction of GABA concentration in the brain causes convolution besides several neurological diseases. The fluorine-containing substrate analogues for the inactivation of the GABA-AT are synthesized extensively in cases where the inactivation mechanisms involve HF elimination. Although two proposed mechanisms are present for the HF elimination, the details of the base-induced HF elimination are not well identified. In this density functional theory (DFT) study, fluorine-containing substrate analogue, 5-amino-2-fluorocyclohex-3-enecarboxylic acid, is particularly chosen in order to explain the details of the HF elimination reactions. On the other hand, the experimental studies revealed that aromatization competes with Michael addition mechanism in the presence of 5-amino-2-fluorocyclohex-3-enecarboxylic acid. The results allowed us to draw a conclusion for the nature of HF elimination, besides the elucidation of the mechanism preference for the inactivation mechanism. Furthermore, the solvent phase calculations carried out in this study ensure that the proton transfer steps should be assisted either by a water molecule or a base for lower activation energy barriers.