Conformational Entropy as Collective Variable for Proteins

Many enhanced sampling methods, such as Umbrella Sampling, Metadynamics or Variationally Enhanced Sampling, rely on the identification of appropriate collective variables. For proteins, even small ones, finding appropriate collective variables has proven challenging. Here we suggest that the NMR $S^2$ order parameter can be used to this effect. We trace the validity of this statement to the suggested relation between $S^2$ and entropy. Using the $S^2$ order parameter and a surrogate for the protein enthalpy in conjunction with Metadynamics or Variationally Enhanced Sampling we are able to reversibly fold and unfold a small protein and draw its free energy at a fraction of the time that is needed in unbiased simulations. From a more conceptual point of view this implies describing folding as a resulting from a trade off between entropy and enthalpy. We also use $S^2$ in combination with the free energy flooding method to compute the unfolding rate of this peptide. We repeat this calculation at different temperatures to obtain the unfolding activation energy.Comment: 4 pages, 3 figure

Enhancing entropy and enthalpy fluctuations to drive crystallization in atomistic simulations

Crystallization is a process of great practical relevance in which rare but crucial fluctuations lead to the formation of a solid phase starting from the liquid. Like in all first order first transitions there is an interplay between enthalpy and entropy. Based on this idea, to drive crystallization in molecular simulations, we introduce two collective variables, one enthalpic and the other entropic. Defined in this way, these collective variables do not prejudge the structure the system is going to crystallize into. We show the usefulness of this approach by studying the case of sodium and aluminum that crystallize in the bcc and fcc crystalline structure, respectively. Using these two generic collective variables, we perform variationally enhanced sampling and well tempered metadynamics simulations, and find that the systems transform spontaneously and reversibly between the liquid and the solid phases.Comment: 4 pages, 2 figure

Frequency adaptive metadynamics for the calculation of rare-event kinetics

The ability to predict accurate thermodynamic and kinetic properties in biomolecular systems is of both scientific and practical utility. While both remain very difficult, predictions of kinetics are particularly difficult because rates, in contrast to free energies, depend on the route taken and are thus not amenable to all enhanced sampling methods. It has recently been demonstrated that it is possible to recover kinetics through so called `infrequent metadynamics' simulations, where the simulations are biased in a way that minimally corrupts the dynamics of moving between metastable states. This method, however, requires the bias to be added slowly, thus hampering applications to processes with only modest separations of timescales. Here we present a frequency-adaptive strategy which bridges normal and infrequent metadynamics. We show that this strategy can improve the precision and accuracy of rate calculations at fixed computational cost, and should be able to extend rate calculations for much slower kinetic processes.Comment: 15 pages, 2 figures, 2 table

Induced hypothermia after cardiac arrest in Iceland

Neðst á síðunni er hægt að nálgast greinina í heild sinni með því að smella á hlekkinn View/OpenObjective: The number of out-of-hospital cardiac arrests in Iceland is about 200/year. In 2002, two prospective randomized trails showed improved outcome when mild hypothermia was induced in a very selective group of comatose patients after cardiac arrest. At Landspitali University Hospital at Hringbraut, hypothermic treatment after a cardiac arrest has been used since Mars 2002. Aim of this study was to: 1) Evaluate outcome of all comatose patients after cardiac arrest in two time periods before and after induced-hypothermia was implemented at our hospital. 2) Estimate how fast and well the patients were cooled with external cooling. Material and methods: 20 patients received hypothermic treatment after resuscitation during the period from mars until December 2002. These patients were compared with 32 other patients who did not receive hypothermic treatment after resuscitation from a cardiac arrest, during the period from January 2000 until March 2002. Information regarding, time from the arrest to beginning of resuscitation (t-1), time from the arrest to return of spontaneous circulation (t-2), time from the arrest until the cooling was actively started (t-3), time from the arrest until the lowest temperature was achieved (t-4), and how many got to the target temperature (32-34°C), where gathered from medical journals. The primary outcome measure was survival to hospital discharge with sufficientlygoodneurologicfunctiontobedischargedtohome or to a rehabilitation facility. Results: 40% of the hypothermic had a good neurologic outcome compared with 28% of the normothermic group. T-1 was 3,2 min. and 3,3 min., t-2 was 35,4 min. and 29,3 min. on average in the hypothermic group and the normothermic group, respectively. T-3 was 2,8 hours and t-4 was 9,8 hours on average in the hypothermic group. 40% of the hypothermic group did not reach target temperature. Conclusion: The results of this study show that 40% of the patients where hypothermia was induced had good neurological outcome compared with 28% of the patients where hypothermia was not induced. In contrast to other studies, the present study included all comatous patients arriving to the hospital after cardiac arrest, regardless of the type of arrythmia and the time from the arrest to return of spontaneus circulation. This study also shows that the cooling technique used is slow and insufficientinachievingthetargettemperature set in this study.Tilgangur: Hjartastopp utan spítala á Íslandi eru um það bil 200/ári. Nýlegar klínískar ranns­­óknir benda til þess að kæling eftir hjartastopp sé taugaverndandi. Á Landspítala við Hringbraut hefur kælingu verið beitt sem meðferð eftir hjartastopp síðan í mars 2002. Til­­gangur þessarar rannsóknar var að: 1) meta áhrif kælingar á afdrif sjúklinga með og án kælingar. 2) meta árangur þess hversu hratt og vel tókst að kæla sjúklingana. Efniviður og aðferðir: Alls voru 20 sjúklingar kældir á tímabilinu mars til desember 2002. Þessir sjúklingar voru bornir saman við 32 sjúklinga sem ekki voru kældir sem lögðust inn eftir hjartastopp á tímabilinu janúar 2000 til mars 2002. Upplýsingar voru fengnar úr sjúkraskrám varðandi; tíma frá áfalli að endur­lífgun (t-1), tíma frá áfalli þar til sjálfvirkt blóðflæði komst á (t-2), tíma frá áfalli þar til kæling er hafin (t-3), tíma frá áfalli þar til lægsta hitastig náðist (t-4) og hversu margir náðu kjörhitastigi (32-34° C). Afdrif sjúklinganna voru metin eftir því hvert þeir út­skrif­uðust. Útkoma var talin góð ef sjúklingur útskrifaðist heim eða á endurhæfingardeild, slæm ef sjúklingur útskrifaðist á langlegudeild eða lést. Niðurstöður: Góð útkoma var skráð hjá 40% kældra saman­borið við 28% ekki kældra. T-1 var 3,2 mínútur og 3,3 mínútur, t-2 var 35,4 mínútur og 29,3 mínútur að meðal­tali hjá kæld­um og ekki kældum, í þessari röð. T-3 var 2,8 klukkustundir og t-4 var 9,8 klukku­stundir að meðal­tali hjá kælda hópnum. 40% sjúklinganna í kælda hópnum fóru ekki undir 34° C. Umræður: Niðurstöður þessarar rannsóknar sýndu að 40% sjúklinga sem voru kældir fengu góðan bata eftir hjartastopp miðað við góðan bata hjá 28% sjúklinga sem ekki voru kældir. Gagnstætt öðrum rannsóknum náði þessi rannsókn til allra sem komu meðvitundarlausir inn á sjúkrahús eftir hjartastopp, óháð tegund hjartsláttaróreglu eða tímalengd frá áfalli. Einnig sýndi rannsóknin að með ytri kælingu gekk illa og hægt að ná því hitastigi sem stefnt var að

Nuances of interpreting X-ray analysis by deep learning and lessons for reporting experimental findings

With the increase in the availability of annotated X-ray image data, there has been an accompanying and consequent increase in research on machine-learning-based, and ion particular deep-learning-based, X-ray image analysis. A major problem with this body of work lies in how newly proposed algorithms are evaluated. Usually, comparative analysis is reduced to the presentation of a single metric, often the area under the receiver operating characteristic curve (AUROC), which does not provide much clinical value or insight and thus fails to communicate the applicability of proposed models. In the present paper, we address this limitation of previous work by presenting a thorough analysis of a state-of-the-art learning approach and hence illuminate various weaknesses of similar algorithms in the literature, which have not yet been fully acknowledged and appreciated. Our analysis was performed on the ChestX-ray14 dataset, which has 14 lung disease labels and metainfo such as patient age, gender, and the relative X-ray direction. We examined the diagnostic significance of different metrics used in the literature including those proposed by the International Medical Device Regulators Forum, and present the qualitative assessment of the spatial information learned by the model. We show that models that have very similar AUROCs can exhibit widely differing clinical applicability. As a result, our work demonstrates the importance of detailed reporting and analysis of the performance of machine-learning approaches in this field, which is crucial both for progress in the field and the adoption of such models in practice.Publisher PDFPeer reviewe

Finding Multiple Reaction Pathways of Ligand Unbinding

Searching for reaction pathways describing rare events in large systems presents a long-standing challenge in chemistry and physics. Incorrectly computed reaction pathways result in the degeneracy of microscopic configurations and inability to sample hidden energy barriers. To this aim, we present a general enhanced sampling method to find multiple diverse reaction pathways of ligand unbinding through non-convex optimization of a loss function describing ligand-protein interactions. The method successfully overcomes large energy barriers using an adaptive bias potential, and constructs possible reaction pathways along transient tunnels without the initial guesses of intermediate or final states, requiring crystallographic information only. We examine the method on the T4 lysozyme L99A mutant which is often used as a model system to study ligand binding to proteins, provide a previously unknown reaction pathway, and show that using the bias potential and the tunnel widths it is possible to capture heterogeneity of the unbinding mechanisms between the found transient protein tunnels

Gas-Phase Retinal Spectroscopy: Temperature Effects Are But a Mirage

We employ state-of-the-art first-principle approaches to investigate whether temperature effects are responsible for the unusually broad and flat spectrum of protonated Schiff base retinal observed in photodissociation spectroscopy, as has recently been proposed. We first carefully calibrate how to construct a realistic geometrical model of retinal and show that the exchange–correlation M06-2X functional yields an accurate description while the commonly used complete active space self-consistent field method (CASSCF) is not adequate. Using modern multiconfigurational perturbative methods (NEVPT2) to compute the excitations, we then demonstrate that conformations with different orientations of the β-ionone ring are characterized by similar excitations. Moreover, other degrees of freedom identified as active in room-temperature molecular dynamics simulations do not yield the shift required to explain the anomalous spectral shape. Our findings indicate that photodissociation experiments are not representative of the optical spectrum of retinal in the gas phase and call for further experimental characterization of the dissociation spectr