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Protein engineering through directed evolution and (semi)rational approaches is routinely applied to optimize protein properties for a broad range of applications in industry and academia. The multitude of possible variants, combined with limited screening throughput, hampers efficient protein engineering. Data-driven strategies have emerged as a powerful tool to model the protein fitness landscape that can be explored in silico , significantly accelerating protein engineering campaigns. However, such methods require a certain amount of data, which often cannot be provided, to generate a reliable model of the fitness landscape. Here, we introduce MERGE, a method that combines direct coupling analysis (DCA) and machine learning (ML). MERGE enables data-driven protein engineering when only limited data are available for training, typically ranging from 50 to 500 labeled sequences. Our method demonstrates remarkable performance in predicting a protein's fitness value and rank based on its sequence across diverse proteins and properties. Notably, MERGE outperforms state-of-the-art methods when only small data sets are available for modeling, requiring fewer computational resources, and proving particularly promising for protein engineers who have access to limited amounts of data.

Protein disorder and aggregation play significant roles in the pathogenesis of numerous neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases. The end products of the aggregation process in these diseases are highly structured amyloid fibrils. Though in most cases, small, soluble oligomers formed during amyloid aggregation are the toxic species. A full understanding of the physicochemical forces that drive protein aggregation is thus required if one aims for the rational design of drugs targeting the formation of amyloid oligomers. Among a multitude of biophysical and biochemical techniques that are employed for studying protein aggregation, molecular dynamics (MD) simulations at the atomic level provide the highest temporal and spatial resolution of this process, capturing key steps during the formation of amyloid oligomers. Here we provide a step-by-step guide for setting up, running, and analyzing MD simulations of aggregating peptides using GROMACS. For the analysis, we provide the scripts that were developed in our lab, which allow to determine the oligomer size and inter-peptide contacts that drive the aggregation process. Moreover, we explain and provide the tools to derive Markov state models and transition networks from MD data of peptide aggregation., (© 2022. Springer Science+Business Media, LLC, part of Springer Nature.)

Data-driven strategies are gaining increased attention in protein engineering due to recent advances in access to large experimental databanks of proteins, next-generation sequencing (NGS), high-throughput screening (HTS) methods, and the development of artificial intelligence algorithms. However, the reliable prediction of beneficial amino acid substitutions, their combination, and the effect on functional properties remain the most significant challenges in protein engineering, which is applied to develop proteins and enzymes for biocatalysis, biomedicine, and life sciences. Here, we present a general-purpose framework (PyPEF: pythonic protein engineering framework) for performing data-driven protein engineering using machine learning methods combined with techniques from signal processing and statistical physics. PyPEF guides the identification and selection of beneficial proteins of a defined sequence space by systematically or randomly exploring the fitness of variants and by sampling random evolution pathways. The performance of PyPEF was evaluated concerning its predictive accuracy and throughput on four public protein and enzyme data sets using common regression models. It was proved that the program could efficiently predict the fitness of protein sequences for different target properties (predictive models with coefficient of determination values ranging from 0.58 to 0.92). By combining machine learning and protein evolution, PyPEF enabled the screening of proteins with various functions, reaching a screening capacity of more than 500,000 protein sequence variants in the timeframe of only a few minutes on a personal computer. PyPEF displayed significant accuracies on four public data sets (different proteins and properties) and underlined the potential of integrating data-driven technologies for covering different philosophies by either predicting the fitness of the variants to the highest accuracy accounting for epistatic effects or capturing the general trend of introduced mutations on the fitness in directed protein evolution campaigns. In essence, PyPEF can provide a powerful solution to current sequence exploration and combinatorial problems faced in protein engineering through exhaustive in silico screening of the sequence space.

Molecular dynamic (MD) simulations are an important tool for studying protein aggregation processes, which play a central role in a number of diseases including Alzheimer's disease. However, MD simulations produce large amounts of data, requiring advanced methods to extract mechanistic insight into the process under study. Transition networks (TNs) provide an elegant method to identify (meta)stable states and the transitions between them from MD simulations. Here, we apply two different methods to generate TNs for protein aggregation: Markov state models (MSMs), which are based on kinetic clustering the state space, and TNs using conformational clustering. The similarities and differences of both methods are elucidated for the aggregation of the fragment Aβ 16-22 of the Alzheimer's amyloid-β peptide. In general, both methods perform excellently in identifying the main aggregation pathways. The strength of MSMs is that they provide a rather coarse and thus simply to interpret picture of the aggregation process. Conformation-sorting TNs, on the other hand, outperform MSMs in uncovering mechanistic details. We thus recommend to apply both methods to MD data of protein aggregation in order to obtain a complete picture of this process. As part of this work, a Python script called ATRANET for automated TN generation based on a correlation analysis of the descriptors used for conformational sorting is made publicly available.

During reproductive maturation, characteristic changes occur in the morphology of the gonadotropin releasing hormone (GnRH) cell population within the hypothalamus. In the early stages of development, GnRH neurons are bipolar cells; however, just before pubertal onset, the majority of these neurons transform into unipolar cells. Our laboratory has reported that valproic acid (VPA), an antiepileptic medication that has previously been shown to slow the velocity of pubertal development in both humans and seizure-prone mice, is capable of delaying the normal process of GnRH morphological differentiation. As VPA is primarily believed to act via a GABAergic mechanism, the present study investigated potential influences of VPA on GnRH-GABA interactions within the medial preoptic area (mPOA) across pubertal development (experiment 1), as well as in adult animals (experiment 2). The results from experiment 1 revealed the expected drug effects on GnRH cell morphology. For VPA animals, there was a greater percentage of bipolar neurons at every time period except for the 24-day sample. Additionally, VPA animals had greater numbers of bipolar and unipolar GnRH neurons with GABA associations across all ages. However, experiment 2 showed a lack of drug effects on GnRH-GABA interactions in adulthood. These results suggest that VPA may delay GnRH cell morphological maturation by altering the density of GABAergic inputs to GnRH neurons. These inputs may normally play a role in timing the activation of the GnRH pulse generator. However, any neuroendocrine effects of VPA in adulthood are most likely due to the actions of VPA at another level of the hypothalamic-pituitary-gonadal axis.

Sodium valproic acid (VPA) is a widely prescribed anticonvulsant medication that has been shown to interfere with pubertal maturation of the reproductive system, and induce endocrine abnormalities in adults, within a subset of the clinical population. While VPA's mechanism of action is still poorly understood, it may exert its anti-reproductive effects by enhancing GABAergic inhibition of the GnRH neuronal population within the medial preoptic area (mPOA). The purpose of this study was to determine if chronic administration of VPA alters GABA levels within the mPOA region. In Experiment 1, the mPOA, caudate, and arcuate nucleus regions were harvested from VPA-treated and control mice. Analysis of whole tissue content of GABA revealed that levels were lower in the caudate and arcuate nucleus regions of VPA-treated animals, whereas there were no group differences for the mPOA region. Collapsing across drug group, there was also a trend for males having overall higher levels of GABA as compared to females. In Experiments 2 and 3, mice were implanted with microdialysis probes within the mPOA region and sampled for extracellular GABA levels. Females (Exp. 3) were sampled either on diestrous, proestrous, or estrous. Results from males (Exp. 2) revealed that VPA enhanced extracellular GABA levels in the mPOA region compared with controls. However, GABA levels for both groups remained stable across the sampling period. Conversely, in Exp. 3, females showed cyclical release of GABA across the sampling period. For control females, GABA levels increased during the afternoon on all cycle days, but the rise on proestrus was smaller than on other cycle days. VPA-treated animals showed an overall reduction in GABA levels compared with controls. Furthermore, while GABA increased over sampling time on estrus and diestrus days of the cycle, there was not a significant rise in GABA on proestrus. These data indicate: (1) regional specificity in VPA effects upon GABA levels, (2) a sex difference in the effects of VPA on GABA levels within the mPOA, and (3) GABA levels increase on the afternoon of all days of the estrous cycle with VPA attenuating the rise seen on the afternoon of proestrus. These results provide evidence that VPA effects upon the reproductive axis may involve changes in GABA release, and that males and females show different patterns of neurochemical response to the drug.

The decline in the release of various anterior pituitary hormones, particularly prolactin (PRL), as a result of exposure to inhibitory, short-day photoperiods in long-day breeders is a well-documented phenomenon. This alteration in the hypothalamic-hypophyseal-gonadal axis is largely controlled by changes in the duration of the nocturnal melatonin pulse secreted by the pineal gland. Increased duration of melatonin secretion serves to increase the inhibitory activity of the tuberoinfundibular dopaminergic (TIDA) neurons, which in turn suppress PRL release and synthesis. However, other hypothalamic factors also appear to stimulate anterior pituitary function, working in concert with changes in TIDA activity to modulate circulating PRL levels under different photoperiod conditions. Past work from this laboratory has suggested that neurochemical changes at the level of the hypothalamic paraventricular nuclei (PVN), particularly changes in norepinephrine (NE) activity, may represent one of these modulatory influences. The current study investigated potential alterations in NF, content within the PVN during the early stages of short-day exposure in Siberian hamsters. In addition, the interaction of NE content with the modulatory dopamine (DA) system was investigated via administration of a DA agonist (CD154) or antagonist (pimozide). Hamsters received CB 154 (500 micrograms), pimozide (45 micrograms), or control solution via the drinking water under either long days (16L:8D) or short days (10L:14D), and were sacrificed at 1, 3, or 5 wk of photoperiod and drug treatment. The brains were removed, and the PVN microdissected from the hypothalamus and analyzed for catecholamine content using high-pressure liquid chromatography with electrochemical detection (HPLC-EC). The results revealed the expected stimulation of circulating PRL under both photoperiods following pimozide administration, as well as the expected trend toward an accelerated suppression of PRL under short days induced by CB 154. For control animals, there were no changes in NE content, or the ratio of 3-methoxy-4-hydroxyphenyglycol (MHPG)/ NE in long-day exposed animalx, but for short-day animals, both NE content and the ratio of MHPG/NE declined over time. Administration of CB 154 had only a marginal effect on NE content and the ratio of MHPG/ NE, but pimozide administration resulted in a decline in NE content and an increase in the MHPG/NE ratio under short days, and an increase in NE content and the MHPG/NE ratio over time for long-day animals. These results suggest that both photoperiod and administration of a DA antagonist that increases circulating PRL levels induce alterations in NE terminal activity within the hypothalamic PVN.