122 research outputs found
Exploring Protein-Protein Interactions as Drug Targets for Anti-cancer Therapy with In Silico Workflows
No full textWe describe a computational protocol to aid the design of small molecule and peptide drugs that target protein-protein interactions, particularly for anti-cancer therapy. To achieve this goal, we explore multiple strategies, including finding binding hot spots, incorporating chemical similarity and bioactivity data, and sampling similar binding sites from homologous protein complexes. We demonstrate how to combine existing interdisciplinary resources with examples of semi-automated workflows. Finally, we discuss several major problems, including the occurrence of drug-resistant mutations, drug promiscuity, and the design of dual-effect inhibitors.Fil: Goncearenco, Alexander. National Institutes of Health; Estados UnidosFil: Li, Minghui. Soochow University; China. National Institutes of Health; Estados UnidosFil: Simonetti, Franco Lucio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Shoemaker, Benjamin A. National Institutes of Health; Estados UnidosFil: Panchenko, Anna R. National Institutes of Health; Estados Unido
VIBRATIONAL ANHARMONICITY AND SCALING THE QUANTUM MECHANICAL MOLECULAR FORCE FIELD
No full text Yu. N. Panchenko, P. Pulay and F. T\""{o}r\""{o}k, J. Mol. Struct. 34, 283 (1976); V.I. Pupyshev, Yu.N. Panchenko, Ch. W. Bock and G. Pongor, J. Chem. Phys. 94, 1247 (1991); Yu. N. Panchenko, G.R. De Mar\'{e} and V.I. Pupyshev, J. Phys. Chem. 99, 17544 (1995); Yu. N. Panchenko, Moscow Univ. Chem. Bull. 51 (5), 23 (1996). D.M. Dennison, Rev. Mod, Phys. 12, 175 (1940); G.E. Hansen and D.M. Dennison, J. Chem. Phys. 20, 313 (1952).Author Institution: Laboratory of Molecular Spectroscopy, Division of Physical Chemistry, Department of Chemistry, M.V. Lomonosov Moscow State University; Laboratory of Molecular Structure and Quantum Mechanics, Division of Physical Chemistry, Department of Chemistry, M.V. Lomonosov Moscow State University; Chemistry Department, Philadelphia College of Textiles \& ScienceThe interrelationship between the scale factors obtained using Pulay's from the anharmonic and the harmonized vibrational frequencies of a light molecule and its heavy analogue is considered in terms of a Morse potential. The determination of the scale factors from the vibrational frequencies of a light molecule is shown to result in smaller deviations of the calculated and experimental vibrational frequencies of its heavy analogue than those of the reverse procedure. In this context the extent to which Dennison's is satisfied is also discussed
TRANSFERABILITY OF PULAY'S SCALE FACTORS IN THE IVa GROUP OF THE MENDELEYEV PERIODIC SYSTEM
No full text P.C. Hariharan and J.A. Pople, Chem. Phys. Lett. 16, 217 (1972). Yu. N. Pancbenko, P. Pulay and F. T\""{o}r\""{o}k, J. Mol. Structure 34, 283 (1976); V.I. Pupyshev, Yu. N. Panchenko, Ch. W. Bock and G. Pongor, J. Chem. Phys. 94, 1247 (1991); Yu. N. Panchenko, G.R. De Mar\'{e} and V.I. Pupyshev, J. Phys. Chem. 99, 17544 (1995); Yu. N. Panchenko, Moscow Univ. Chem. Bull. 51 (5), 23 (1996).Author Institution: Laboratory of Molecular Spectroscopy, Division of Physical Chemistry, Department of Chemistry, M.V. Lomonosov Moscow State University; Laboratoire de Chimie Physique Mol\'{e}culaire, Facult\'{e} des Sciences, CP 160/09, Universit\'{e} Libre de Bruxelles; Laboratory of Molecular Structure and Quantum Mechanics, Division of Physical Chemistry, Department of Chemistry, M.V. Lomonosov Moscow State University, Moscow 119899, Russian Federation.Ab initio quantum mechanical calculations were performed for structures and force fields of 3,3-dimethylbutene-1, cyclopropene, 1-methylcyclopropene, and 1-trimethylsilyl-, 1,2-bis(trimethylsilyl)-, 1-trimethylgermyl-, 1,2-bis(trimethylgermyl)-, 1-trimethylstannyl-, and 1,2-bis(trimethylstannyl)-3,3-dimethylcyclopropene. Scale factors for correction of the quantum mechanical force fields of cyclopropene, 1-methylcyclopropene, and 3,3-dimethylbutene-1 were determined using Pulay's scaling Only the experimental vibrational frequencies of the light isotopomers of these molecules were used in the scaling procedure. The set of scale factors obtained was transferred to the quantum mechanical force fields of all the other molecules mentioned above. The vibrational problems for these molecules were solved. Complete vibrational analyses were carried out for the whole set of these related compounds. Transferability of scale factors for series of related compounds of cyclopropene with heteroatoms from the IVa group of the Mendeleyev Periodic System of chemical elements was demonstrated
Accurate cancer phenotype prediction with AKLIMATE, a stacked kernel learner integrating multimodal genomic data and pathway knowledge
No full textAdvancements in sequencing have led to the proliferation of multi-omic profiles of human cells under different conditions and perturbations. In addition, many databases have amassed information about pathways and gene "signatures"-patterns of gene expression associated with specific cellular and phenotypic contexts. An important current challenge in systems biology is to leverage such knowledge about gene coordination to maximize the predictive power and generalization of models applied to high-throughput datasets. However, few such integrative approaches exist that also provide interpretable results quantifying the importance of individual genes and pathways to model accuracy. We introduce AKLIMATE, a first kernel-based stacked learner that seamlessly incorporates multi-omics feature data with prior information in the form of pathways for either regression or classification tasks. AKLIMATE uses a novel multiple-kernel learning framework where individual kernels capture the prediction propensities recorded in random forests, each built from a specific pathway gene set that integrates all omics data for its member genes. AKLIMATE has comparable or improved performance relative to state-of-the-art methods on diverse phenotype learning tasks, including predicting microsatellite instability in endometrial and colorectal cancer, survival in breast cancer, and cell line response to gene knockdowns. We show how AKLIMATE is able to connect feature data across data platforms through their common pathways to identify examples of several known and novel contributors of cancer and synthetic lethality
State of the art: refinement of multiple sequence alignments
No full textAbstract Correction to Chakrabarti S, Lanczycki CJ, Panchenko AR, Przytycka TM, Thiessen PA and Bryant SH: State of the art: refinement of multiple sequence alignments. BMC Bioinformatics 2006, 7:499.</p
Structural similarity of loops in protein families: toward the understanding of protein evolution
No full textAbstract Background Protein evolution and protein classification are usually inferred by comparing protein cores in their conserved aligned parts. Structurally aligned protein regions are separated by less conserved loop regions, where sequence and structure locally deviate from each other and do not superimpose well. Results Our results indicate that even longer protein loops can not be viewed as "random coils" and for the majority of protein families in our test set there exists a linear correlation between the measures of sequence similarity and loop structural similarity. Results suggest that distance matrices derived from the loop (dis)similarity measure may produce in some cases more reliable cluster trees compared to the distance matrices based on the conventional measures of sequence and structural (dis)similarity. Conclusions We show that by considering "dissimilar" loop regions rather than only conserved core regions it is possible to improve our understanding of protein evolution.</p
Ensemble approach to predict specificity determinants: benchmarking and validation
No full textAbstract Background It is extremely important and challenging to identify the sites that are responsible for functional specification or diversification in protein families. In this study, a rigorous comparative benchmarking protocol was employed to provide a reliable evaluation of methods which predict the specificity determining sites. Subsequently, three best performing methods were applied to identify new potential specificity determining sites through ensemble approach and common agreement of their prediction results. Results It was shown that the analysis of structural characteristics of predicted specificity determining sites might provide the means to validate their prediction accuracy. For example, we found that for smaller distances it holds true that the more reliable the prediction method is, the closer predicted specificity determining sites are to each other and to the ligand. Conclusion We observed certain similarities of structural features between predicted and actual subsites which might point to their functional relevance. We speculate that majority of the identified potential specificity determining sites might be indirectly involved in specific interactions and could be ideal target for mutagenesis experiments.</p
Structural and functional roles of coevolved sites in proteins.
No full textUnderstanding the residue covariations between multiple positions in protein families is very crucial and can be helpful for designing protein engineering experiments. These simultaneous changes or residue coevolution allow protein to maintain its overall structural-functional integrity while enabling it to acquire specific functional modifications. Despite the significant efforts in the field there is still controversy in terms of the preferable locations of coevolved residues on different regions of protein molecules, the strength of coevolutionary signal and role of coevolution in functional diversification.In this paper we study the scale and nature of residue coevolution in maintaining the overall functionality and structural integrity of proteins. We employed a large scale study to investigate the structural and functional aspects of coevolved residues. We found that the networks representing the coevolutionary residue connections within our dataset are in general of 'small-world' type as they have clustering coefficient values higher than random networks and also show smaller mean shortest path lengths similar and/or lower than random and regular networks. We also found that altogether 11% of functionally important sites are coevolved with any other sites. Active sites are found more frequently to coevolve with any other sites (15%) compared to protein (11%) and ligand (9%) binding sites. Metal binding and active sites are also found to be more frequently coevolved with other metal binding and active sites, respectively. Analysis of the coupling between coevolutionary processes and the spatial distribution of coevolved sites reveals that a high fraction of coevolved sites are located close to each other. Moreover, approximately 80% of charge compensatory substitutions within coevolved sites are found at very close spatial proximity (<or= 5A), pointing to the possible preservation of salt bridges in evolution.Our findings show that a noticeable fraction of functionally important sites undergo coevolution and also point towards compensatory substitutions as a probable coevolutionary mechanism within spatially proximal coevolved functional sites
Finding weak similarities between proteins by sequence profile comparison
No full textTo improve the recognition of weak similarities between proteins a method of aligning two sequence pro®les is proposed. It is shown that exploring the sequence space in the vicinity of the sequence with unknown properties signi®cantly improves the performance of sequence alignment methods. Consistent with the previous observations the recognition sensitivity and alignment accuracy obtained by a pro®le±pro®le alignment method can be as much as 30 % higher compared to the sequence±pro®le alignment method. It is demon-strated that the choice of score function and the diversity of the test pro®le are very important factors for achieving the maximum performance of the method, whereas the optimum range of these parameters depends on the level of similarity to be recognized
TRAINING SOCIOLOGY STUDENTS IN COMPUTER ANALYSIS OF DEMOGRAPHIC PROCESSES AND STRUCTURE
No full textIn the modern era of digital globalization, it is becoming more and more important to train sociology students in the field of demographics and demographic statistics based not only on demographic theories but also on the practical application of the new computer tools and technologies, databases and Internet services. The article analyzes the capabilities of modern computer tools for the analysis of demographic processes and structures in training sociology students; substantiates the use of the R environment as a tool for analysis and graphical representation of demographic data. It presents the idea of teaching students to perform computer analysis of demographic data using a combination of Excel spreadsheets, SPSS statistical package, R environment illustrated by two examples. The first example concerns building and comparing the gender-age pyramid of the population of Ukraine at different years and includes searching for the relevant data, building the pyramid using standard diagram building Excel tools, using SPSS tools (Chart Builder, Histogram, Population Pyramid), and using pyramid package of R environment. The second example relates to calculation of childcare and grandparent care load coefficients, visualizing their dynamics, and includes an introduction to the demographic passport of Ukraine. The article presents the developed methodological support for teaching sociology students to perform demographic data analysis, including presentation-lectures on the fundamental principles of work in R and R Studio environment, laboratory works (theory summary, detailed operative instructions, control questions, tasks for students ‘ independent work); data packages attached to every assignment. The author has analyzed the didactic capabilities of the free Gapminder service that includes the list of the tools titled `Play with Data`, bubble chart, maps, ranking, trends, age pyramids. This provides colorful and dynamic data visualization for chosen demographic criteria (depending on the research objectives) by countries and continents over time that stimulates the students to conduct additional scientific research
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