Improvement of molecular-replacement models with Sculptor.

In molecular replacement, the quality of models can be improved by transferring information contained in sequence alignment to the template structure. A family of algorithms has been developed that make use of the sequence-similarity score calculated from residue-substitution scores smoothed over nearby residues to delete or downweight parts of the model that are unreliable. These algorithms have been implemented in the program Sculptor, together with well established methods that are in common use for model improvement. An analysis of the new algorithms has been performed by studying the effect of algorithm parameters on the quality of models. Benchmarking against existing techniques shows that models from Sculptor compare favourably, especially if the alignment is unreliable. Carrying out multiple trials using alternative models created from the same structure but using different algorithm parameters can significantly improve the success rate

Local error estimates dramatically improve the utility of homology models for solving crystal structures by molecular replacement.

Predicted structures submitted for CASP10 have been evaluated as molecular replacement models against the corresponding sets of structure factor amplitudes. It has been found that the log-likelihood gain score computed for each prediction correlates well with common structure quality indicators but is more sensitive when the accuracy of the models is high. In addition, it was observed that using coordinate error estimates submitted by predictors to weight the model can improve its utility in molecular replacement dramatically, and several groups have been identified who reliably provide accurate error estimates that could be used to extend the application of molecular replacement for low-homology cases.Support received from the NIH (grant P01GM063210), the Wellcome Trust (Principal Research Fellowship to R.J.R., grant 082961/Z/07/Z; Strategic Award to the Cambridge Institute for Medical Research), as well as from the Swedish Research Council (621-2012-5270), Swedish e-Science Research Center, and Carl Tryggers Stiftelse to B.W. is gratefully acknowledged.This is the final published version. It first appeared at http://www.sciencedirect.com/science/article/pii/S0969212614004146#

Konzisztencia-alapú jövőkutatási modellek fejlesztése (KJM) = Development of consistency-based models for futurology

A konzisztencia-alapú jövőkutatási modellek fejlesztése című projekt céljait/feladatait a http://miau.gau.hu/miau/79/otkakjm2.doc tartalmazza. Az elméleti eredmények értelmében minden olyan területen lehetséges az emberi intuíció/heurisztika forráskódba zárása, vagyis tetszőleges célú robotok alkotása, ahol van tanulásra alkalmas mintázat (vö. OAM). A robot sikeressége elsődlegesen a tanulási minta mibenlétén múlik, mivel a tanulási folyamat (vö. hasonlóságelemzés) tetszőleges eszközökre támaszkodhat (pl. additív, multiplikatív, hibrid modellek), s a keresés vezérlési probléma is becsatornázható az optimalizáló algoritmusok hatékonyságot biztosító medrébe. A tanulási folyamat és az OAM értékének értelmezése önmagában is egy konzisztencia-probléma (vö. ceteris paribus elvárások). A tanulás minősítését nem célszerű a hagyományos (adatelkülönítő, mintázat-pazarló) teszteléssel végezni (http://miau.gau.hu/miau/111/chf30.doc), ehelyett univerzális (pl. több egybe hangzó érv, leghosszabb logikai lánc, ill. rendszer-stabilitási elv) és/vagy probléma-specifikus (vö. ár/teljesítmény optimum fogalma: http://miau.gau.hu/myx-free/index.php3?x=i0100) elvek alapján, több egymásra rétegződő (a valóság átfedéses aspektusait feltáró: http://miau.gau.hu/miau/116/szigma_plrf.doc) modell formájában kell biztosítani. Gyakorlati oldalról mindez online keretek között is realizálható (http://my-x.hu). A projekt megteremtette az érintett elméletek szintézisének operacionalizálási kereteit. | The document http://miau.gau.hu/miau/79/otkakjm2.doc contains the aims and topics of project 'Development of consistency-based models for futurology'. The creation of arbitrary virtual robots (based on the intuition and heuristics of human and transformed into source codes) is possible, if an OAM (including learning patterns) is given. The benefit through a robot is basically depending on the quality of the learning pattern, because the learn process (c.f. similarity analysis) can combine arbitrary resources (e.g. additive and multiplicative models) and optimizing algorithms (ensuring efficiency) can be involved as search strategy. The interpretation of an OAM and the learn process is itself a consistency-problem (c.f. ceteris paribus expectations). The quality of a learn process should not be evaluated through the classic test logic separating and wasting data. Instead of tests (http://miau.gau.hu/miau/111/chf30.doc) multi-layer analyses should be realized basing on universal principles (c.f. majority, long of logical chain, system-stability) and /or problem-specific expectation (c.f. price-performance optimum: http://miau.gau.hu/myx-free/index.php3?x=i0100) providing model overlapping effects (http://miau.gau.hu/miau/116/szigma_plrf.doc). All these services can be ensured also online (http://my-x.hu). Therefore the project make possible (c.f. operationalize) to realize synthesis of concerned theories

Improved estimates of coordinate error for molecular replacement.

The estimate of the root-mean-square deviation (r.m.s.d.) in coordinates between the model and the target is an essential parameter for calibrating likelihood functions for molecular replacement (MR). Good estimates of the r.m.s.d. lead to good estimates of the variance term in the likelihood functions, which increases signal to noise and hence success rates in the MR search. Phaser has hitherto used an estimate of the r.m.s.d. that only depends on the sequence identity between the model and target and which was not optimized for the MR likelihood functions. Variance-refinement functionality was added to Phaser to enable determination of the effective r.m.s.d. that optimized the log-likelihood gain (LLG) for a correct MR solution. Variance refinement was subsequently performed on a database of over 21,000 MR problems that sampled a range of sequence identities, protein sizes and protein fold classes. Success was monitored using the translation-function Z-score (TFZ), where a TFZ of 8 or over for the top peak was found to be a reliable indicator that MR had succeeded for these cases with one molecule in the asymmetric unit. Good estimates of the r.m.s.d. are correlated with the sequence identity and the protein size. A new estimate of the r.m.s.d. that uses these two parameters in a function optimized to fit the mean of the refined variance is implemented in Phaser and improves MR outcomes. Perturbing the initial estimate of the r.m.s.d. from the mean of the distribution in steps of standard deviations of the distribution further increases MR success rates

Phaser.MRage: automated molecular replacement.

Phaser.MRage is a molecular-replacement automation framework that implements a full model-generation workflow and provides several layers of model exploration to the user. It is designed to handle a large number of models and can distribute calculations efficiently onto parallel hardware. In addition, phaser.MRage can identify correct solutions and use this information to accelerate the search. Firstly, it can quickly score all alternative models of a component once a correct solution has been found. Secondly, it can perform extensive analysis of identified solutions to find protein assemblies and can employ assembled models for subsequent searches. Thirdly, it is able to use a priori assembly information (derived from, for example, homologues) to speculatively place and score molecules, thereby customizing the search procedure to a certain class of protein molecule (for example, antibodies) and incorporating additional biological information into molecular replacement

Az információs rendszerek szerepe és értékelése a vállalkozásokban

A vállalat belső és a környezetével lebonyolított információkat tároló, feldolgozó és a megfelelő döntéshozónak rendelkezésére bocsájtó vállalati információs rendszer alkalmazása és az informatikai beruházások az adott általánosságban a cég fejlettségét, piaci versenyképességét is jelzik. A magyar vállalkozások körében azonban a bevezetett ERP rendszerek száma alacs onyabb, mint az Európai Unió tagállamaiban átlagosan, melynek egyik oka a bevezetés költségeitől való félelem. Bemutatjuk, hogy milyen módszertanok mentén vizsgálható az infokommunikációs technológiákba történő beruházások értékelése

Macromolecular X-ray structure determination using weak, single-wavelength anomalous data.

We describe a likelihood-based method for determining the substructure of anomalously scattering atoms in macromolecular crystals that allows successful structure determination by single-wavelength anomalous diffraction (SAD) X-ray analysis with weak anomalous signal. With the use of partial models and electron density maps in searches for anomalously scattering atoms, testing of alternative values of parameters and parallelized automated model-building, this method has the potential to extend the applicability of the SAD method in challenging cases

Comparison of automated crystallographic model-building pipelines

A comparison of four protein model-building pipelines (ARP/wARP, Buccaneer, PHENIX AutoBuild and SHELXE) was performed using data sets from 202 experimentally phased cases, both with the data as observed and truncated to simulate lower resolutions. All pipelines were run using default parameters. Additionally, an ARP/wARP run was completed using models from Buccaneer. All pipelines achieved nearly complete protein structures and low Rwork/Rfree at resolutions between 1.2 and 1.9 Å, with PHENIX AutoBuild and ARP/wARP producing slightly lower R factors. At lower resolutions, Buccaneer leads to significantly more complete models

Automating crystallographic structure solution and refinement of protein-ligand complexes.

High-throughput drug-discovery and mechanistic studies often require the determination of multiple related crystal structures that only differ in the bound ligands, point mutations in the protein sequence and minor conformational changes. If performed manually, solution and refinement requires extensive repetition of the same tasks for each structure. To accelerate this process and minimize manual effort, a pipeline encompassing all stages of ligand building and refinement, starting from integrated and scaled diffraction intensities, has been implemented in Phenix. The resulting system is able to successfully solve and refine large collections of structures in parallel without extensive user intervention prior to the final stages of model completion and validation