CaMELS: In silico prediction of calmodulin binding proteins and their binding sites

Due to Ca²⁺‐dependent binding and the sequence diversity of Calmodulin (CaM) binding proteins, identifying CaM interactions and binding sites in the wet‐lab is tedious and costly. Therefore, computational methods for this purpose are crucial to the design of such wet‐lab experiments. We present an algorithm suite called CaMELS (CalModulin intEraction Learning System) for predicting proteins that interact with CaM as well as their binding sites using sequence information alone. CaMELS offers state of the art accuracy for both CaM interaction and binding site prediction and can aid biologists in studying CaM binding proteins. For CaM interaction prediction, CaMELS uses protein sequence features coupled with a large‐margin classifier. CaMELS models the binding site prediction problem using multiple instance machine learning with a custom optimization algorithm which allows more effective learning over imprecisely annotated CaM‐binding sites during training. CaMELS has been extensively benchmarked using a variety of data sets, mutagenic studies, proteome‐wide Gene Ontology enrichment analyses and protein structures. Our experiments indicate that CaMELS outperforms simple motif‐based search and other existing methods for interaction and binding site prediction. We have also found that the whole sequence of a protein, rather than just its binding site, is important for predicting its interaction with CaM. Using the machine learning model in CaMELS, we have identified important features of protein sequences for CaM interaction prediction as well as characteristic amino acid sub‐sequences and their relative position for identifying CaM binding sites. Python code for training and evaluating CaMELS together with a webserver implementation is available at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#camels .     

Interactive effects of protein and carbohydrates on production of microbial metabolites in the large intestine of growing pigs

The study aimed at determining the effect of protein type and indigestible carbohydrates on the concentration of microbial metabolites in the large intestine of pigs. The experiment involved 36 pigs (15 kg initial body weight) divided into six groups, fed cereal-based diets with highly digestible casein (CAS) or potato protein concentrate (PPC) of lower ileal digestibility. Each diet was supplemented with cellulose, raw potato starch or pectin. After 2 weeks of feeding, pigs were sacrificed and samples of caecal and ascending, transverse and descending colon digesta were collected for analyses of microbial metabolites. PPC increased the concentration of ammonia, p-cresol, indole, n-butyrate, isovalerate and most of the amines in comparison with CAS. Pectin reduced the production of p-cresol, indole, phenylethylamine and isovalerate in the large intestine compared with potato starch. Starch and pectin increased mainly the concentration of n-butyrate and n-valerate in the colon compared to cellulose. Interaction affected mainly amines. Feeding PPC diet with potato starch considerably increased putrescine, cadaverine, tyramine and total amines concentrations compared with PPC diets with pectin and cellulose, whereas feeding CAS diet with starch reduced their concentrations. There was also a significant effect of interaction between diet and intestinal segment on microbial metabolites. In conclusion, PPC intensifies proteolysis in the large intestine and also n-butyrate production. Raw starch and pectin similarly increase n-butyrate concentration but pectin inhibits proteolysis more efficiently than starch. The interactive effects of both factors indicate that pectin and cellulose may beneficially affect fermentative processes in case of greater protein flow to the large intestine.     

The optimal density of cellular solids in axial tension

For cellular bodies with uniform cell size, wall thickness, and shape, an important question is whether the same volume of material has the same effect when arranged as many small cells or as fewer large cells. To answer this question, for finite element models of periodic structures of Mooney-type material with different structural geometry and subject to large strain deformations, we identify a nonlinear elastic modulus as the ratio between the mean effective stress and the mean effective strain in the solid cell walls, and show that this modulus increases when the thickness of the walls increases, as well as when the number of cells increases while the volume of solid material remains fixed. Since, under the specified conditions, this nonlinear elastic modulus increases also as the corresponding mean stress increases, either the mean modulus or the mean stress can be employed as indicator when the optimum wall thickness or number of cells is sought.     

Advancement in high dose therapy and autologous stem cell rescue in lymphoma

Although advanced stage aggressive non-Hodgkin’s lymphomas and Hodgkin’s disease are thought to be chemotherapy-responsive cancers, a considerable number of patients either relapse or never attain a remission. High-dose therapy (HDT) followed by autologous stem cell transplantation (ASCT) is often the only possibility of cure for most of these patients. However, many controversial issues still remain with respect to HDT/ASCT for lymphomas, including its role for, the optimal timing of transplantation, the best conditioning regimen and the potential use of localized radiotherapy or immunologic methods to decrease post-transplant recurrence. Recently, mainly due to the unavailability of carmustine, several novel conditioning protocols have been clinically developed, with the aim of improving the overall outcome by enhancing the anti-lymphoma effect and, at the same time, by reducing short and long-term toxicity. Furthermore, the better safety profiles of novel approaches would definitively allow patients aged more than 65-70 years to benefit from this therapeutic option. In this review, we will briefly discuss the most relevant and recent data available regarding HDT/ASCT in lymphomas.     

EB病毒诱发人B细胞淋巴瘤的分子病理特性

EB病毒 (EBV ,Epstein Barrvirus)与人类多种肿瘤有关 ,尤其是与鼻咽癌和淋巴瘤的关系密切。为此研究了EB病毒在huPBL SCID嵌合体小鼠体内诱发人B细胞淋巴瘤的分子特性及肿瘤发生机制。从健康成人外周血分离出淋巴细胞 ,将之移植到SCID小鼠腹腔内 ,实验感染EBV ,观察肿瘤的形成 ;采用单向免疫扩散法连续监测小鼠血清中人IgG的含量。分别用PCR方法检测肿瘤组织中是否存在人Alu序列 ,原位杂交检测肿瘤组织中EB病毒小RNA分子EBER 1;免疫组织化学方法检测人白细胞分化抗原 (CD4 5、CD2 0、CD4 5RO、CD3) ,病毒基因(LMP1、EBNA2、BZLF1)的表达 ,以及细胞瘤基因蛋白 (p5 3、C myc、Bcl 2、Bax)在诱发肿瘤中的表达情况。结果发现 ,实验中 34只感染EBV的huPBL SCID小鼠有 2 4只诱发出肿瘤 ,根据病理形态学特征、Alu PCR和免疫标志均证实诱发瘤是人源性B淋巴细胞肿瘤。原位分子杂交显示肿瘤细胞核内存在EBER 1,少数瘤细胞表达EB病毒BZLF1蛋白阳性 ,部分瘤细胞表达LMP1和EBNA2蛋白阳性。连续监测 12只huPBL SCID小鼠血清中人IgG含量 ,发现IgG水平随诱瘤时间延长和肿瘤生长有逐渐增高趋势。免疫组化显示诱发的 2 4例淋巴瘤组织p5 3、C myc、Bcl 2和Bax蛋白表达阳性率分别为 83.33%、10 0 %、95 .83%、91.6 7%。结果     

Statistical potentials for fold assessment

A protein structure model generally needs to be evaluated to assess whether or not it has the correct fold. To improve fold assessment, four types of a residue-level statistical potential were optimized, including distance-dependent, contact, Phi/Psi dihedral angle, and accessible surface statistical potentials. Approximately 10,000 test models with the correct and incorrect folds were built by automated comparative modeling of protein sequences of known structure. The criterion used to discriminate between the correct and incorrect models was the Z-score of the model energy. The performance of a Z-score was determined as a function of many variables in the derivation and use of the corresponding statistical potential. The performance was measured by the fractions of the correctly and incorrectly assessed test models. The most discriminating combination of any one of the four tested potentials is the sum of the normalized distance-dependent and accessible surface potentials. The distance-dependent potential that is optimal for assessing models of all sizes uses both C(alpha) and C(beta) atoms as interaction centers, distinguishes between all 20 standard residue types, has the distance range of 30 A, and is derived and used by taking into account the sequence separation of the interacting atom pairs. The terms for the sequentially local interactions are significantly less informative than those for the sequentially nonlocal interactions. The accessible surface potential that is optimal for assessing models of all sizes uses C(beta) atoms as interaction centers and distinguishes between all 20 standard residue types. The performance of the tested statistical potentials is not likely to improve significantly with an increase in the number of known protein structures used in their derivation. The parameters of fold assessment whose optimal values vary significantly with model size include the size of the known protein structures used to derive the potential and the distance range of the accessible surface potential. Fold assessment by statistical potentials is most difficult for the very small models. This difficulty presents a challenge to fold assessment in large-scale comparative modeling, which produces many small and incomplete models. The results described in this study provide a basis for an optimal use of statistical potentials in fold assessment.