Neural models for predicting viral vaccine targets

We applied artificial neural networks (ANN) for the prediction of targets of immune responses that are useful for study of vaccine formulations against viral infections. Using a novel data representation, we developed a system termed MULTIPRED that can predict peptide binding to multiple related human leukocyte antigens (HLA). This implementation showed high accuracy in the prediction of the promiscuous peptides that bind to five HLA-A2 allelic variants. MULTIPRED is useful for the identification of peptides that bind multiple HLA-A2 variants as a group. By implementing ANN as a classification engine, we enabled both the prediction of peptides binding to multiple individual HLA-A2 molecules and the prediction of promiscuous binders using a single model. The ANN MULTIPRED predicts peptide binding to HLA-A*0205 with excellent accuracy (area under the receiver operating characteristic curve--AROC>0.90), and to HLA-A*0201, HLA-A*0204 and HLA-A*0206 with high accuracy (AROC>0.85). Antigenic regions with high density of binders ("antigenic hot-spots") represent best targets for vaccine design. MULTIPRED not only predicts individual 9-mer binders but also predicts antigenic hot spots. Two HLA-A2 hot-spots in Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) membrane protein were predicted by using MULTIPRED.     

Identification and characterization of merozoite surface protein 1 epitope

Malaria is an important tropical infection which urgently requires intervention of an effective vaccine. Antigenic variations of the parasite and allelic diversity of the host are main problems in the development of an effective malaria vaccine. Cytotoxic T lymphocytes (CTL) directed against Plasmodium falciparum­derived antigens are shown to play an important role for the protection against malaria. The merozoite surface protein 1 (MSP1) is expressed in all the four life-cycle stages of Plasmodium falciparum and did not find any sequence similarity to human and mouse reference proteins. MSP1 is a known target of the immune response and a single CTL epitope binding to the HLA­A*0201 is available for merozoite form. Here, we report the results from the computational characterization of MSP1, precursor (1720 residue) and screening of highest scoring potential CTL epitopes for 1712 overlapping peptides binding to thirty four HLA class­I alleles and twelve HLA class­I supertypes (5 HLA­A and 7 HLA­B) using bioinformatics tools. Supertypes are the clustered groups of HLA class­I molecules, representing a sets of molecules that share largely overlapping peptide binding specificity. The prediction results for MSP1 as adhesin and adhesin-like in terms of probability is 1.0. Results also show that MSP1 has orthologs to other related species as well as having non allergenicity and single transmembrane properties demonstrating its suitability as a vaccine candidate. The predicted peptides are expected to be useful in the design of multi-epitope vaccines without compromising the human population coverage.     

Methods for prediction of peptide binding to MHC molecules: a comparative study

BACKGROUND: A variety of methods for prediction of peptide binding to major histocompatibility complex (MHC) have been proposed. These methods are based on binding motifs, binding matrices, hidden Markov models (HMM), or artificial neural networks (ANN). There has been little prior work on the comparative analysis of these methods. MATERIALS AND METHODS: We performed a comparison of the performance of six methods applied to the prediction of two human MHC class I molecules, including binding matrices and motifs, ANNs, and HMMs. RESULTS: The selection of the optimal prediction method depends on the amount of available data (the number of peptides of known binding affinity to the MHC molecule of interest), the biases in the data set and the intended purpose of the prediction (screening of a single protein versus mass screening). When little or no peptide data are available, binding motifs are the most useful alternative to random guessing or use of a complete overlapping set of peptides for selection of candidate binders. As the number of known peptide binders increases, binding matrices and HMM become more useful predictors. ANN and HMM are the predictive methods of choice for MHC alleles with more than 100 known binding peptides. CONCLUSION: The ability of bioinformatic methods to reliably predict MHC binding peptides, and thereby potential T-cell epitopes, has major implications for clinical immunology, particularly in the area of vaccine design.     

Improving MHC binding peptide prediction by incorporating binding data of auxiliary MHC molecules

MOTIVATION: Various computational methods have been proposed to tackle the problem of predicting the peptide binding ability for a specific MHC molecule. These methods are based on known binding peptide sequences. However, current available peptide databases do not have very abundant amounts of examples and are highly redundant. Existing studies show that MHC molecules can be classified into supertypes in terms of peptide-binding specificities. Therefore, we first give a method for reducing the redundancy in a given dataset based on information entropy, then present a novel approach for prediction by learning a predictive model from a dataset of binders for not only the molecule of interest but also for other MHC molecules. RESULTS: We experimented on the HLA-A family with the binding nonamers of A1 supertype (HLA-A*0101, A*2601, A*2902, A*3002), A2 supertype (A*0201, A*0202, A*0203, A*0206, A*6802), A3 supertype (A*0301, A*1101, A*3101, A*3301, A*6801) and A24 supertype (A*2301 and A*2402), whose data were collected from six publicly available peptide databases and two private sources. The results show that our approach significantly improves the prediction accuracy of peptides that bind a specific HLA molecule when we combine binding data of HLA molecules in the same supertype. Our approach can thus be used to help find new binders for MHC molecules.     

Towards the interaction mechanism of tocopherols and tocotrienols (vitamin E) with selected metabolizing enzymes

Vitamin E is a mixture of eight compounds α, β, γ, δ­ tocopherols and α, β, γ, δ­ tocotrienols. Their individual role in cellular transport as antioxidants and in metabolic pathways has been highlighted in the present work. All the eight compounds have been docked with the respective metabolizing enzymes (α­tocopherol transfer protein (ATTP), α­tocopherol associated protein (TAP), P­glycoprotein (P­gly) and human serum albumin (HSA)) to understand molecular interactions for pharmacokinetics. These have been structurally aligned against the four human phospholipids in order to reveal their individual role in chylomicron formation and hence the mechanism of cellular transport. The study of their binding with their metabolizing enzymes provides insight to the comparative antioxidant activity of each of these isomers.     

Human iPSC‐derived neural precursor cells differentiate into multiple cell types to delay disease progression following transplantation into YAC128 Huntington's disease mouse model

ObjectivesTo investigate whether human HLA‐homozygous induced pluripotent stem cell (iPSC)‐derived neural precursor cells (iPSC‐NPCs) can provide functional benefits in Huntington’s disease (HD), we transplanted them into the YAC128 transgenic HD mouse model.Materials and MethodsCHAi001‐A, an HLA‐homozygous iPSC line (A*33:03‐B*44:03‐DRB1*13:02), was differentiated into neural precursor cells, and then, they were transplanted into 6 months‐old YAC128 mice. Various behavioural and histological analyses were performed for five months after transplantation.ResultsMotor and cognitive functions were significantly improved in transplanted animals. Cells transplanted in the striatum showed multipotential differentiation. Five months after transplantation, the donor cells had differentiated into neurons, oligodendrocytes and astrocytes. Transplantation restored DARPP‐32 expression, synaptophysin density, myelin basic protein expression in the corpus callosum and astrocyte function.ConclusionAltogether, these results strongly suggest that iPSC‐NPCs transplantation induces neuroprotection and functional recovery in a mouse model of HD and should be taken forward for clinical trials in HD patients.     

Coupling in silico and in vitro analysis of peptide-MHC binding: a bioinformatic approach enabling prediction of superbinding peptides and anchorless epitopes

The ability to define and manipulate the interaction of peptides with MHC molecules has immense immunological utility, with applications in epitope identification, vaccine design, and immunomodulation. However, the methods currently available for prediction of peptide-MHC binding are far from ideal. We recently described the application of a bioinformatic prediction method based on quantitative structure-affinity relationship methods to peptide-MHC binding. In this study we demonstrate the predictivity and utility of this approach. We determined the binding affinities of a set of 90 nonamer peptides for the MHC class I allele HLA-A*0201 using an in-house, FACS-based, MHC stabilization assay, and from these data we derived an additive quantitative structure-affinity relationship model for peptide interaction with the HLA-A*0201 molecule. Using this model we then designed a series of high affinity HLA-A2-binding peptides. Experimental analysis revealed that all these peptides showed high binding affinities to the HLA-A*0201 molecule, significantly higher than the highest previously recorded. In addition, by the use of systematic substitution at principal anchor positions 2 and 9, we showed that high binding peptides are tolerant to a wide range of nonpreferred amino acids. Our results support a model in which the affinity of peptide binding to MHC is determined by the interactions of amino acids at multiple positions with the MHC molecule and may be enhanced by enthalpic cooperativity between these component interactions.     

A hybrid approach for predicting promiscuous MHC class I restricted T cell epitopes

In the present study, a systematic attempt has been made to develop an accurate method for predicting MHC class I restricted T cell epitopes for a large number of MHC class I alleles. Initially, a quantitative matrix (QM)-based method was developed for 47 MHC class I alleles having at least 15 binders. A secondary artificial neural network (ANN)-based method was developed for 30 out of 47 MHC alleles having a minimum of 40 binders. Combination of these ANN-and QM-based prediction methods for 30 alleles improved the accuracy of prediction by 6% compared to each individual method. Average accuracy of hybrid method for 30 MHC alleles is 92.8%. This method also allows prediction of binders for 20 additional alleles using QM that has been reported in the literature, thus allowing prediction for 67 MHC class I alleles. The performance of the method was evaluated using jack-knife validation test. The performance of the methods was also evaluated on blind or independent data. Comparison of our method with existing MHC binder prediction methods for alleles studied by both methods shows that our method is superior to other existing methods. This method also identifies proteasomal cleavage sites in antigen sequences by implementing the matrices described earlier. Thus, the method that we discover allows the identification of MHC class I binders (peptides binding with many MHC alleles) having proteasomal cleavage site at C-terminus. The user-friendly result display format (HTML-II) can assist in locating the promiscuous MHC binding regions from antigen sequence. The method is available on the web at www.imtech.res.in/raghava/nhlapred and its mirror site is available at http://bioinformatics.uams.edu/mirror/nhlapred/.     

Efficacy and acceptability of pharmacological,psychosocial, and brain stimulation interventions in children and adolescents with mental disorders: an umbrella review

Top‐tier evidence on the safety/tolerability of 80 medications in children/adolescents with mental disorders has recently been reviewed in this jour­nal. To guide clinical practice, such data must be combined with evidence on efficacy and acceptability. Besides medications, psychosocial inter­ventions and brain stimulation techniques are treatment options for children/adolescents with mental disorders. For this umbrella review, we systematically searched network meta‐analyses (NMAs) and meta‐analyses (MAs) of randomized controlled trials (RCTs) evaluating 48 medications, 20 psychosocial interventions, and four brain stimulation techniques in children/adolescents with 52 different mental disorders or groups of mental disorders, reporting on 20 different efficacy/acceptability outcomes. Co‐primary outcomes were disease‐specific symptom reduction and all‐cause discontinuation (“acceptability”). We included 14 NMAs and 90 MAs, reporting on 15 mental disorders or groups of mental disorders. Overall, 21 medications outperformed placebo regarding the co‐primary outcomes, and three psychosocial interventions did so (while seven outperformed waiting list/no treatment). Based on the meta‐analytic evidence, the most convincing efficacy profile emerged for amphetamines, methylphenidate and, to a smaller extent, behavioral therapy in attention‐deficit/hyperactivity disorder; aripiprazole, risperidone and several psychosocial interventions in autism; risperidone and behavioral interventions in disruptive behavior disorders; several antipsychotics in schizophrenia spectrum disorders; fluoxetine, the combination of fluoxetine and cognitive behavioral therapy (CBT), and interpersonal therapy in depression; aripiprazole in mania; fluoxetine and group CBT in anxiety disorders; fluoxetine/selective serotonin reuptake inhibitors, CBT, and behavioral therapy with exposure and response prevention in obsessive‐compulsive disorder; CBT in post‐traumatic stress disorder; imipramine and alarm behavioral intervention in enuresis; behavioral therapy in encopresis; and family therapy in anorexia nervosa. Results from this umbrella review of interventions for mental disorders in children/adolescents provide evidence‐based information for clinical decision making.     

Random screening of proteins for HLA-A*0201-binding nine-amino acid peptides is not sufficient for identifying CD8 T cell epitopes recognized in the context of HLA-A*0201

HLA-A2 is the most frequent HLA molecule in Caucasians with HLA-A*0201 representing the most frequent allele; it was also the first human HLA allele for which peptide binding prediction was developed. The Bioinformatics and Molecular Analysis Section of the National Institutes of Health (BIMAS) and the University of Tübingen (Syfpeithi) provide the most popular prediction algorithms of peptide/MHC interaction on the World Wide Web. To test these predictions, HLA-A*0201-binding nine-amino acid peptides were searched by both algorithms in 19 structural CMV proteins. According to Syfpeithi, the top 2% of predicted peptides should contain the naturally presented epitopes in 80% of predictions (www.syfpeithi.de). Because of the high number of predicted peptides, the analysis was limited to 10 randomly chosen proteins. The top 2% of peptides predicted by both algorithms were synthesized corresponding to 261 peptides in total. PBMC from 10 HLA-A*0201-positive and CMV-seropositive healthy blood donors were tested by ex vivo stimulation with all 261 peptides using crossover peptide pools. IFN-gamma production in T cells measured by CFC was used as readout. However, only one peptide was found to be stimulating in one single donor. As a result of this work, we report a potential new T cell target protein, one previously unknown CD8-T cell-stimulating peptide, and an extensive list of CMV-derived potentially strong HLA-A*0201-binding peptides that are not recognized by T cells of HLA-A*0201-positive CMV-seropositive donors. We conclude that MHC/peptide binding predictions are helpful for locating epitopes in known target proteins but not necessarily for screening epitopes in proteins not known to be T cell targets.     

Identification of noncanonical melanoma-associated T cell epitopes for cancer immunotherapy

The identification of tumor-associated T cell epitopes has contributed significantly to the understanding of the interrelationship of tumor and immune system and is instrumental in the development of therapeutic vaccines for the treatment of cancer. Most of the known epitopes have been identified with prediction algorithms that compute the potential capacity of a peptide to bind to HLA class I molecules. However, naturally expressed T cell epitopes need not necessarily be strong HLA binders. To overcome this limitation of the available prediction algorithms we established a strategy for the identification of T cell epitopes that include suboptimal HLA binders. To this end, an artificial neural network was developed that predicts HLA-binding peptides in protein sequences by taking the entire sequence context into consideration rather than computing the sum of the contribution of the individual amino acids. Using this algorithm, we predicted seven HLA A*0201-restricted potential T cell epitopes from known melanoma-associated Ags that do not conform to the canonical anchor motif for this HLA molecule. All seven epitopes were validated as T cell epitopes and three as naturally processed by melanoma tumor cells. T cells for four of the new epitopes were found at elevated frequencies in the peripheral blood of melanoma patients. Modification of the peptides to the canonical sequence motifs led to improved HLA binding and to improved capacity to stimulate T cells.     

Ab initio prediction of peptide-MHC binding geometry for diverse class I MHC allotypes

Since determining the crystallographic structure of all peptide-MHC complexes is infeasible, an accurate prediction of the conformation is a critical computational problem. These models can be useful for determining binding energetics, predicting the structures of specific ternary complexes with T-cell receptors, and designing new molecules interacting with these complexes. The main difficulties are (1) adequate sampling of the large number of conformational degrees of freedom for the flexible peptide, (2) predicting subtle changes in the MHC interface geometry upon binding, and (3) building models for numerous MHC allotypes without known structures. Whereas previous studies have approached the sampling problem by dividing the conformational variables into different sets and predicting them separately, we have refined the Biased-Probability Monte Carlo docking protocol in internal coordinates to optimize a physical energy function for all peptide variables simultaneously. We also imitated the induced fit by docking into a more permissive smooth grid representation of the MHC followed by refinement and reranking using an all-atom MHC model. Our method was tested by a comparison of the results of cross-docking 14 peptides into HLA-A*0201 and 9 peptides into H-2K(b) as well as docking peptides into homology models for five different HLA allotypes with a comprehensive set of experimental structures. The surprisingly accurate prediction (0.75 A backbone RMSD) for cross-docking of a highly flexible decapeptide, dissimilar to the original bound peptide, as well as docking predictions using homology models for two allotypes with low average backbone RMSDs of less than 1.0 A illustrate the method's effectiveness. Finally, energy terms calculated using the predicted structures were combined with supervised learning on a large data set to classify peptides as either HLA-A*0201 binders or nonbinders. In contrast with sequence-based prediction methods, this model was also able to predict the binding affinity for peptides to a different MHC allotype (H-2K(b)), not used for training, with comparable prediction accuracy.     

Prediction of MHC class I binding peptides, using SVMHC

Background  

T-cells are key players in regulating a specific immune response. Activation of cytotoxic T-cells requires recognition of specific peptides bound to Major Histocompatibility Complex (MHC) class I molecules. MHC-peptide complexes are potential tools for diagnosis and treatment of pathogens and cancer, as well as for the development of peptide vaccines. Only one in 100 to 200 potential binders actually binds to a certain MHC molecule, therefore a good prediction method for MHC class I binding peptides can reduce the number of candidate binders that need to be synthesized and tested.     

Internalizing psychopathology and all‐cause mortality: a comparison of transdiagnostic vs. diagnosis‐based risk prediction

Previous studies have documented the utility of a transdiagnostic internalizing factor in predicting important future outcomes (e.g., subsequent mental disorder diagnoses). To date, however, no study has investigated whether an internalizing factor predicts mortality risk. Also, while pre­vious studies of mortality risk have emphasized its associations with particular internalizing disorders, no study has assessed how the transdiagnostic internalizing factor vs. disorder‐specific variance differently predict that risk. The primary aims of this study were to explore: a) whether the internalizing factor predicts mortality risk, b) whether particular internalizing psychopathologies uniquely predict mortality risk over and beyond the transdiagnostic internalizing factor, and c) whether there is a significant interaction of internalizing with self‐reported health in the prediction of mortality risk. We utilized a large national sample of American adults from the Midlife in the United States (MIDUS), a longitudinal study that examined midlife development of individuals across multiple waves between 1995 and 2015. Data were analyzed for the 6,329 participants who completed the phone interview and self‐administered questionnaire in MIDUS 1 (1995‐1996) and were then followed up until October 31, 2015 or until death. To investigate the association between internalizing and mortality risk, we used the semi‐parametric proportional hazards Cox model, where survival time was regressed on a latent internalizing factor. Overall findings indicate that a transdiagnostic internalizing factor significantly predicts mortality risk over a 20‐year period (hazard ratio, HR=1.12, 95% CI: 1.05‐1.16, p<0.01) and that internalizing outperforms disorder‐specific variance (e.g., depression‐specific variance) in the prediction of that risk. Further, there was a significant interaction between transdiagnostic internalizing and self‐reported health, whereby internalizing psychopathology had a specific association with early death for individuals with excellent self‐reported health condition (HR=1.50, 95% CI: 1.17‐1.84, p<0.05). This highlights the clinical utility of using the transdiagnostic internalizing factor for prediction of an important future outcome, and supports the argument that internalizing psychopathology can be a meaningful liability to explore in public health practice.     

PINAT1.0: Protein interaction network analysis tool

Cellular processes are regulated by interaction of various proteins i.e. multiprotein complexes and absences of these interactions are often the cause of disorder or disease. Such type of protein interactions are of great interest for drug designing. In host­parasite diseases like Tuberculosis, non-homologous proteins as drug target are first preference. Most potent drug target can be identifying among large number of non-homologous protein through protein interaction network analysis. Drug target should be those non-homologous protein which is associated with maximum number of functional proteins i.e. has highest number of interactants, so that maximum harm can be caused to pathogen only. In present work, Protein Interaction Network Analysis Tool (PINAT) has been developed to identification of potential protein interaction for drug target identification. PINAT is standalone, GUI application software made for protein-protein interaction (PPI) analysis and network building by using co­evolutionary profile. PINAT is very useful for large data PPI study with easiest handling among available softwares. PINAT provides excellent facilities for the assembly of data for network building with visual presentation of the results and interaction score. The software is written in JAVA and provides reliability through transparency with user.

Availability

PINAT is available at www.manit.ac.in/pinat     

Recent population differentiation in the habitat specialist Glossy Antshrike (Aves: Thamnophilidae) across Amazonian seasonally flooded forests

We assessed population structure and the spatio‐temporal pattern of diversification in the Glossy Antshrike Sakesphorus luctuosus (Aves, Thamnophilidae) to understand the processes shaping the evolutionary history of Amazonian floodplains and address unresolved taxonomic controversies surrounding its species limits. By targeting ultraconserved elements (UCEs) from 32 specimens of S. luctuosus, we identified independent lineages and estimated their differentiation, divergence times, and migration rates. We also estimated current and past demographic histories for each recovered lineage. We found evidence confirming that S. luctuosus consists of a single species, comprising at least four populations, with some highly admixed individuals and overall similar levels of migration between populations. We confirmed the differentiation of the Araguaia River basin population (S. l. araguayae) and gathered circumstantial evidence indicating that the taxon S. hagmanni may represent a highly introgressed population between three distinct phylogroups of S. luctuosus. Divergences between populations occurred during the last 1.2 mya. Signs of population expansions were detected for populations attributed to subspecies S. l. luctuosus, but not for the S. l. araguayae population. Our results support that S. luctuosus has had a complex population history, resulting from a high dependence on southeastern “clear water” seasonally flooded habitats and their availability through time. Spatial and demographic expansions toward the western “white water” flooded forests might be related to recent changes in connectivity and availability of these habitats. Our study reinforces the view that isolation due to absence of suitable habitat has been an important driver of population differentiation within Amazonian flooded forests, but also that differences between várzeas (“white water” floodplains, mostly in southwestern Amazonia) and igapós (“clear water” floodplains, especially located in the east) should be further explored as drivers of micro‐evolution for terrestrial species.     

Investigation of peptide involvement in T cell allorecognition using recombinant HLA class I multimers

Alloreactive T cells are involved in injurious graft rejection and graft-vs-host disease. However, they can also evoke beneficial responses to tumor Ags restricted by foreign MHC molecules. Manipulation of these alloreactivities requires information on the basis of T cell allorecognition. The vigorous T cell response to foreign MHC molecules may arise from peptide-independent recognition of polymorphic residues of foreign MHC molecules or peptide-specific recognition of novel peptides presented by foreign MHC molecules. We investigated CD8+ T cell allorecognition using recombinant HLA class I/peptide complexes. Peptide-specific allorecognition was examined using tetramers of HLA-A*0201 representing five peptides derived from ubiquitously expressed self-proteins that are known to bind endogenously to HLA-A*0201. Distinct subsets of CD8+ T cells specific for each HLA-A*0201/peptide combination were detected within four in vitro-stimulated T cell populations specific for foreign HLA-A*0201. Peptide-independent allorecognition was investigated using artificial Ag-presenting constructs (aAPCs) coated with CD54, CD80, and functional densities of a single HLA-A*0201/peptide combination for four different peptides. None of the four T cell populations specific for foreign HLA-A*0201 were stimulated by the aAPCs, whereas they did produce IFN-gamma upon stimulation with cells naturally expressing HLA-A*0201. Thus, aAPCs did not stimulate putative peptide-independent allorestricted T cells. The results show that these alloreactive populations comprise subsets of T cells, each specific for a self-peptide presented by foreign class I molecules, with no evidence of peptide-independent components.     

Knowledge-based virtual screening of HLA-A*0201-restricted CD8+ T-cell epitope peptides from herpes simplex virus genome

A novel knowledge-based method is developed to virtually screen potential HLA-A?0201 binders from large-scale peptide candidates. This method utilizes the information from both the crystal structures and experimental affinities of various peptides bound with HLA-A*0201 to construct a single-position mutation free energy profile for accurately characterizing HLA-A*0201-peptide interaction and for effectively predicting the binding affinities of peptides to HLA-A*0201. We employ this method to analyze physicochemical properties and structural implication underlying the specific recognition and association between the HLA-A*0201 and a large panel of peptide segments generated from the herpes simplex virus type 1 (HSV-1) genome, and to evaluate the binding potencies of these peptide candidates to HLA-A*0201. As a result, 288 out of 38,020 candidates are predicted as the potential high-affinity binders of HLA-A*0201, from which three most promising peptides are picked out for further development of potent vaccines against HSV-1. In addition, we also demonstrate that this newly proposed method can successfully identify 8 known binders and 3 known nonbinders from the glycoproteins D and K of HSV-1.     

Edaphic heterogeneity and the evolutionary trajectory of Amazonian plant communities

We investigated how the phylogenetic structure of Amazonian plant communities varies along an edaphic gradient within the non‐inundated forests. Forty localities were sampled on three terrain types representing two kinds of soil: clayey soils of a high base cation concentration derived from the Solimões formation, and loamy soils with lower base cation concentration derived from the Içá formation and alluvial terraces. Phylogenetic community metrics were calculated for each locality for ferns and palms both with ferns as one group and for each of three fern clades with a crown group age comparable to that of palms. Palm and fern communities showed significant and contrasting phylogenetic signals along the soil gradient. Fern species richness increased but standard effect size of mean pairwise distance (SES.MPD) and variation of pairwise distances (VPD) decreased with increasing soil base cation concentration. In contrast, palm communities were more species rich on less cation‐rich soils and their SES.MPD increased with soil base cation concentration. Species turnover between the communities reflected the soil gradient slightly better when based on species occurrences than when phylogenetic distances between the species were considered. Each of the three fern subclades behaved differently from each other and from the entire fern clade. The fern clade whose phylogenetic patterns were most similar to those of palms also resembled palms in being most species‐rich on cation‐poor soils. The phylogenetic structuring of local plant communities varies along a soil base cation concentration gradient within non‐inundated Amazonian rain forests. Lineages can show either similar or different phylogenetic community structure patterns and evolutionary trajectories, and we suggest this to be linked to their environmental adaptations. Consequently, geological heterogeneity can be expected to translate into a potentially highly diverse set of evolutionarily distinct community assembly pathways in Amazonia and elsewhere.