A Large-Scale Test of Free-Energy Simulation Estimates of Protein-Ligand Binding Affinities.

We have performed a large-scale test of alchemical perturbation calculations with the Bennett acceptance-ratio (BAR) approach to estimate relative affinities for the binding of 107 ligands to 10 different proteins. Employing 20-Å truncated spherical systems and only one intermediate state in the perturbations, we obtain an error of less than 4 kJ/mol for 54% of the studied relative affinities and a precision of 0.5 kJ/mol on average. However, only four of the proteins gave acceptable errors, correlations, and rankings. The results could be improved by using nine intermediate states in the simulations or including the entire protein in the simulations using periodic boundary conditions. However, 27 of the calculated affinities still gave errors of more than 4 kJ/mol, and for three of the proteins the results were not satisfactory. This shows that the performance of BAR calculations depends on the target protein and that several transformations gave poor results owing to limitations in the molecular-mechanics force field or the restricted sampling possible within a reasonable simulation time. Still, the BAR results are better than docking calculations for most of the proteins

The Normal-Mode Entropy in the MM/GBSA Method: Effect of System Truncation, Buffer Region, and Dielectric Constant

We have performed a systematic study of the entropy term in the MM/GBSA (molecular Mechanics combined with generalized Born and surface area solvation) approach to calculate ligand-binding affinities The entropies are calculated by a normal mode analysis of harmonic frequencies from minimized snapshots of molecular dynamics simulations. For computational reasons, these calculations have normally been performed on truncated systems. We have studied the binding of eight inhibitors of blood clotting factor Xa, nine ligands of ferritin, and two ligands of HIV-1 protease and show that removing protein residues with. distances. larger than 8-16 angstrom to the ligand, including a 4 angstrom shell of fixed protein residues and water molecules, change the absolute entropies by 1-5 kJ/mol on average. However, the change is systematic, so relative entropies for different ligands change by only 0.7-1.6 kJ/mol on average. Consequently, entropies from truncated systems give relative binding affinities that are identical to those obtained for the Whole protein within statistical uncertainty (172 kJ/mol). We have also tested to use a distance dependent dielectric constant in the minimization and. frequency calculation (epsilon = 4r), but it typically gives slightly different entropies and poorer binding, affinities. Therefore, we recommend entropies calculated with the smallest truncation radius (8 angstrom) and epsilon =1 Such an approach also gives an improved precision for the calculated binding free energies

私の見たアメリカ合衆國(昭和五年十一月小樽市教育會主催講演會講演)

説

Multi-protocol IoT network reconnaissance

Network reconnaissance is a core security functionality, which can be used to detect hidden unauthorized devices or to identify missing devices. Currently, there is a lack of network reconnaissance tools capable of discovering Internet of Things (IoT) devices across multiple protocols. To bridge this gap, we introduce IoT-Scan, an extensible IoT network reconnaissance tool. IoT - Scan is based on software-defined radio (SDR) technology, which allows for a flexible implementation of radio protocols. We propose passive, active, multi-channel, and multi-protocol scanning algorithms to speed up the discovery of devices with IoT-Scan. We implement the scanning algorithms and compare their performance with four popular IoT protocols: Zigbee, Bluetooth LE, Z-Wave, and LoRa. Through experiments with dozens of IoT devices, we demonstrate that our implementation experiences minimal packet losses, and achieves performance near a theoretical benchmark.CNS-1908087 - National Science Foundation; CCF-2006628 - National Science Foundation; ECCS-2128517 - National Science Foundation; CNS-1717858 - National Science FoundationAccepted manuscrip

Investigation of sorption properties in crushed autoclaved aerated concrete waste

Due to hardening in autoclaves and mechanical processing of autoclaved aerated concrete (AAC) massive, the process of production of AAC unavoidably generates waste. Up to now, there were no ways for utilisation of this type of waste. The article deals with the adsorption effectiveness of crushed autoclaved aerated concrete waste (CAACW). It was established that the ability of CAACW to adsorb certain liquids (water, diesel fuel, used engine oil) depends on viscosity of liquid which, in its turn, influences the depth of adsorption. Subject to this index, the CAACW was divided into two fractions: powder (size up to 2.50 mm) and crumbs (size from 2.50 to 10.0 mm). It was found that oil products of different kinematic viscosity are fully adsorbed by CAACW powder, i.e. diesel fuel 0.52 g/g in 18 min, and used engine oil 0.39 g/g in 1 h 15 min. The CAACW crumbs, processed by 2.00% FeSO4 solution and dried to 3.50% of residual moisture are suitable as litter for cats. The practical use of CAACW will help us solve two important environmental problems: on the one hand – to recover the industrial waste, on the other – to prevent ground pollution by effused oil products. Santrauka Autoklavinio akytojo betono (AAC) gamybos proceso metu kietinus autoklave ir gautą masyvą mechaniškai apdirbus neišvengiamai susidaro atliekos, kurios iki šiol nėra tinkamai perdirbamos. Straipsnyje nagrinėjama smulkintų AAC atliekų sorbcinė geba. Nustatyta, kad kai kurių skysčių (vandens, dyzelino, vartotos mašininės alyvos) sugėrimo efektyvumas priklauso nuo skysčio klampio, kuris savo ruožtu lemia skysčio įsigėrimo gylį į sorbento bandinius. Atsižvelgiant į šį rodiklį, smulkintos AAC atliekos buvo suskirstytos į dvi frakcijas: miltelius (dydis iki 2,5 mm) ir trupinius (dydis nuo 2,5 iki 10,0 mm). Nustatyta, kad skirtingo klampio naftos produktai įgeriami AAC milteliais: dyzelinas – 0,52 g/g per 18 min, o vartota mašininė alyva – 0,39 g/g per 1 h 15 min. Trupintos AAC atliekos, apdorotos 2,0% koncentracijos FeSO4 tirpalu ir išdžiovintos iki 3,5% likutinio masės drėgnio, yra tinkamos naudoti kaip kačių kraikas. Praktinis trupintų AAC atliekų naudojimas leis iš karto spręsti dvi svarbias ekologines problemas: pirmuoju atveju – utilizuoti pramonines atliekas, antruoju – stabdyti grunto taršą dėl išsiliejusių naftos produktų. Резюме В процессе изготовления автоклавного ячеистого бетона после твердения в автоклаве с последующей механической обработкой массива неизбежно накапливаются отходы, которые до сих пор должным образом не утилизировались. В статье представлены результаты исследования сорбционной способности измельченных отходовавтоклавного ячеистого бетона. Установлено, что эффективность поглощения некоторых жидкостей (воды, дизелина, отработанного машинного масла) этими отходами зависит от вязкости жидкости, которая, в свою очередь,определяет глубину поглощения жидкости образцами сорбента. В зависимости от этого показателя отходы ячеистого бетона были разделены на две фракции: порошок (крупность частиц составляла до 2,5мм) и крошку (крупность частиц – от 2,5 до 10 мм). Установлено, что жидкие нефтяные продукты в зависимости от их кинетическойвязкости полностью насыщают порошкообразные отходы, а именно: дизелин – 0,52 г/г за 18 мин, отработанноемашинное масло – 0,39 г/г за 1ч 15 мин. Крошкообразные отходы, обработанные 2,0%м раствором FeSO4 и высушенные до 3,5% остаточной массовой влажности могут применяться в качестве наполнителя для кошачьих туалетов. Практическое применение измельченных отходов автоклавного ячеистого бетона позволяет одновременнорешить две экологические проблемы: вопервых, утилизировать промышленные отходы, а вовторых, предотвратить загрязнение грунта разлившимися нефтепродуктами. Reikšminiai žodžiai: autoklavinis akytasis betonas, pramonėsatliekos, sorbentai, sorbcinės savybės, kalcio hidrosilikatai, makrostruktūra, naftos produktai, kačių kraikas Ключевые слова: автоклавный ячеистый бетон, промышленные отходы, сорбенты, сорбционные свойства, гидросиликаты кальция, макроструктура, нефтяные продукты, наполнитель для кошачьих туалето

Prediction of broad-spectrum pathogen attachment to coating materials for biomedical devices

Bacterial infections in healthcare settings are a frequent accompaniment to both routine procedures such as catheterization and surgical site interventions. Their impact is becoming even more marked as the numbers of medical devices that are used to manage chronic health conditions and improve quality of life increases. The resistance of pathogens to multiple antibiotics is also increasing, adding an additional layer of complexity to the problems of employing safe and effective medical procedures. One approach to reducing the rate of infections associated with implanted and indwelling medical devices is the use of polymers that resist the formation of bacterial biofilms. To significantly accelerate the discovery of such materials, we show how state of the art machine learning methods can generate quantitative predictions for the attachment of multiple pathogens to a large library of polymers in a single model for the first time. Such models facilitate design of polymers with very low pathogen attachment across different bacterial species that will be candidate materials for implantable or indwelling medical devices such as urinary catheters, cochlear implants and pacemakers

Predicting protein–ligand binding affinity and correcting crystal structures with quantum mechanical calculations: lactate dehydrogenase A

Accurately computing the geometry and energy of host–guest and protein–ligand interactions requires a physically accurate description of the forces in action. Quantum mechanics can provide this accuracy but the calculations can require a prohibitive quantity of computational resources. The size of the calculations can be reduced by including only the atoms of the receptor that are in close proximity to the ligand. We show that when combined with log P values for the ligand (which can be computed easily) this approach can significantly improve the agreement between computed and measured binding energies. When the approach is applied to lactate dehydrogenase A, it can make quantitative predictions about conformational, tautomeric and protonation state preferences as well as stereoselectivity and even identifies potential errors in structures deposited in the Protein Data Bank for this enzyme. By broadening the evidence base for these structures from only the diffraction data, more chemically realistic structures can be proposed